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Dimension estimates for badly approximable affine forms

Published online by Cambridge University Press:  06 November 2024

TAEHYEONG KIM*
Affiliation:
Department of Mathematical Sciences, Seoul National University, Seoul, South Korea
WOOYEON KIM
Affiliation:
Department of Mathematics, ETH Zürich, Zürich, Switzerland (e-mail: [email protected])
SEONHEE LIM
Affiliation:
Department of Mathematical Sciences and Resesarch Institute of Mathematics, Seoul National University, Seoul, South Korea (e-mail: [email protected])
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Abstract

For given $\epsilon>0$ and $b\in \mathbb {R}^m$, we say that a real $m\times n$ matrix A is $\epsilon $-badly approximable for the target b if

$$ \begin{align*}\liminf_{q\in\mathbb{Z}^n, \|q\|\to\infty} \|q\|^n \langle Aq-b\rangle^m \geq \epsilon,\end{align*} $$
where $\langle \cdot \rangle $ denotes the distance from the nearest integral vector. In this article, we obtain upper bounds for the Hausdorff dimensions of the set of $\epsilon $-badly approximable matrices for fixed target b and the set of $\epsilon $-badly approximable targets for fixed matrix A. Moreover, we give a Diophantine condition of A equivalent to the full Hausdorff dimension of the set of $\epsilon $-badly approximable targets for fixed A. The upper bounds are established by effectivizing entropy rigidity in homogeneous dynamics, which is of independent interest. For the A-fixed case, our method also works for the weighted setting where the supremum norms are replaced by certain weighted quasinorms.

Type
Original Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

1 Introduction

In classical Diophantine approximation, one wants to approximate an irrational number $\alpha $ by rationals $p/q$ for $p,q \in \mathbb {Z}$ . Dirichlet theorem says that for every $N \in \mathbb {N}$ , there exist $p,q \in \mathbb {Z}$ with $0<q<N$ , such that

$$ \begin{align*}|q\alpha-p|<1/N < 1/q.\end{align*} $$

In this way, one can see classical Diophantine approximation as studying distribution of $q\alpha $ modulo $\mathbb {Z}$ near zero. Diophantine approximation for irrational numbers has been generalized to investigating vectors, linear forms, and more generally matrices, and have become classical subjects in metric number theory.

In this article, we consider the inhomogeneous Diophantine approximation: the distribution of $q\alpha $ modulo $\mathbb {Z}$ near a ‘target’ $b \in \mathbb {R}$ . Although Dirichlet theorem does not hold anymore, there exist infinitely many $q\in \mathbb {Z}$ such that

$$ \begin{align*} |q\alpha-b-p| < 1/|q| \quad\text{for some }p\in\mathbb{Z} \end{align*} $$

for almost every $(\alpha ,b)\in \mathbb {R}^2$ and, moreover,

$$ \begin{align*} \liminf_{p,q\in\mathbb{Z}, |q|\to \infty} |q||q\alpha -b-p|=0 \end{align*} $$

for almost every $(\alpha ,b)\in \mathbb {R}^2$ by inhomogeneous Khintchine theorem [Reference CasselsCas57, Theorem II in Ch. VII].

Similarly to numbers, for an $m \times n$ real matrix $A\in M_{m,n}(\mathbb {R})$ , we study $Aq \in \mathbb {R}^m$ modulo $\mathbb {Z}^m$ near the target $b \in \mathbb {R}^m$ for vectors $q \in \mathbb {Z}^n$ . In this general situation as well, using inhomogeneous Khintchine–Groshev theorem ([Reference SchmidtSch64, Theorem 1] or [Reference SprindžukSpr79, Ch. 1, Theorem 15]), we have

$$ \begin{align*}\liminf_{q\in\mathbb{Z}^n, \|q\|\to \infty} \|q\|^{n}\langle Aq-b\rangle^{m}=0\end{align*} $$

for almost every $(A,b) \in {M}_{m,n}(\mathbb {R}) \times \mathbb {R}^m$ . Here, $\langle v\rangle \overset {\operatorname {def}}{=}\inf _{p\in \mathbb {Z}^m} \|v-p\|$ denotes the distance from $v \in \mathbb {R}^m$ to the nearest integral vector with respect to the supremum norm $\|\cdot \|$ .

The exceptional set of the above equality is our object of interest.

1.1 Main results

We will consider the exceptional set with weights in the following sense. Let us first fix, throughout the paper, an m-tuple and an n-tuple of positive reals $\mathbf {r}=(r_1,\ldots ,r_m)$ , $\mathbf {s}=(s_1,\ldots ,s_n)$ such that $r_1\geq \cdots \geq r_m$ , $s_1\geq \cdots \geq s_n$ , and $\sum _{1\leq i\leq m}r_i=1=\sum _{1\leq j\leq n}s_j$ . The special case where $r_i=1/m$ and $s_j=1/n$ for all $i=1,\ldots ,m$ and $j=1,\ldots ,n$ is called the unweighted case.

Define the $\mathbf {r}$ -quasinorm of $\mathbf {x}\in \mathbb {R}^m$ and $\mathbf {s}$ -quasinorm of $\mathbf {y}\in \mathbb {R}^n$ by

$$ \begin{align*}\|\mathbf{x}\|_{\mathbf{r}}\overset{\operatorname{def}}{=}\max_{1\leq i\leq m}|x_i|^{{1}/{r_i}} \quad\textrm{and}\quad \|\mathbf{y}\|_{\mathbf{s}}\overset{\operatorname{def}}{=}\max_{1\leq j\leq n}|y_j|^{{1}/{s_j}}.\end{align*} $$

Denote $\langle \mathbf {x}\rangle _{\mathbf {r}}\overset {\operatorname {def}}{=}\inf _{p\in \mathbb {Z}^m} \|\mathbf {x}-p\|_{\mathbf {r}}$ . We call A, $\epsilon $ -bad for $b\in \mathbb {R}^m$ if

$$ \begin{align*} \liminf_{q\in\mathbb{Z}^n, \|q\|_{\mathbf{r}} \to \infty} \|q\|_{\mathbf{s}}\langle Aq-b\rangle_{\mathbf{r}}\ge \epsilon. \end{align*} $$

Denote

$$ \begin{align*} \mathbf{Bad}(\epsilon)&\overset{\operatorname{def}}{=}\{(A,b)\in {M}_{m,n}(\mathbb{R}) \times \mathbb{R}^m :A\ \mathrm{is}\ \epsilon\text{-}\mathrm{bad\ for}\ b\},\\ \mathbf{Bad}_A(\epsilon)&\overset{\operatorname{def}}{=}\{b\in\mathbb{R}^m:A\ \mathrm{is}\ \epsilon\text{-}\mathrm{bad\ for}\ b\}, \;\;\mathbf{Bad}_A\overset{\operatorname{def}}{=}\bigcup_{\epsilon>0}\mathbf{Bad}_A(\epsilon),\\ \mathbf{Bad}^b(\epsilon)&\overset{\operatorname{def}}{=}\{A\in M_{m,n}(\mathbb{R}):A\ \mathrm{is}\ \epsilon\text{-}\mathrm{bad\ for}\ b\}, \;\; \mathbf{Bad}^b\overset{\operatorname{def}}{=}\bigcup_{\epsilon>0}\mathbf{Bad}^b(\epsilon). \end{align*} $$

The set $\mathbf {Bad}^0$ can be seen as the set of badly approximable systems of m linear forms in n variables. This set is of Lebesgue measure zero [Reference GroshevGro38], but has full Hausdorff dimension $mn$ [Reference SchmidtSch69]. See [Reference Kristensen, Thorn and VelanKTV06, Reference Kleinbock and WeissKW10, Reference Pollington and VelaniPV02] for the weighted setting.

For any b, $\mathbf {Bad}^b$ also has zero Lebesgue measure [Reference SchmidtSch66] and full Hausdorff dimension for every b [Reference Einsiedler and TsengET11]. Indeed, it is shown that $\mathbf {Bad}^b$ is a winning set [Reference Einsiedler and TsengET11] and even a hyperplane winning set [Reference Hussain, Kristensen and SimmonsHKS20], a property which implies full Hausdorff dimension. However, the set $\mathbf {Bad}_A$ also has full Hausdorff dimension for every A [Reference Bugeaud, Harrap, Kristensen and VelaniBHKV10]. See [Reference Bengoechea and MoshchevitinBM17, Reference HarrapHar12, Reference Harrap and MoshchevitinHM17] for the weighted setting.

The sets $\mathbf {Bad}^b$ and $\mathbf {Bad}_A$ are unions of subsets $\mathbf {Bad}^b(\epsilon )$ and $\mathbf {Bad}_A(\epsilon )$ over $\epsilon>0$ , respectively, and thus a more refined question is whether the Hausdorff dimension of $\mathbf {Bad}^b(\epsilon )$ , $\mathbf {Bad}_A(\epsilon )$ could still be of full dimension. For the homogeneous case ( $b=0$ ), the Hausdorff dimension $\mathbf {Bad}^0(\epsilon )$ is less than the full dimension $mn$ (see [Reference Broderick and KleinbockBK13, Reference SimmonsSim18] for the unweighted case and [Reference Kleinbock and MirzadehKM19] for the weighted case). Thus, a natural question is whether $\mathbf {Bad}^b(\epsilon )$ can have full Hausdorff dimension for some b. Our first main result says that in the unweighted case, $\mathbf {Bad}^b(\epsilon )$ cannot have full Hausdorff dimension for any b. We provide an effective bound on the dimension in terms of $\epsilon $ as well.

Theorem 1.1. For the unweighted case, that is, $r_i=1/m$ and $s_j=1/n$ for all $i=1,\ldots ,m$ and $j=1,\ldots ,n$ , there exist $c_0>0$ and $M_0>0$ depending only on d such that for any $\epsilon>0$ and $b\in \mathbb {R}^m$ ,

$$ \begin{align*}\dim_H \mathbf{Bad}^b(\epsilon)\leq mn-c_0\epsilon^{M_0}.\end{align*} $$

As for the set $\mathbf {Bad}_A(\epsilon )$ , the third author, together with U. Shapira and N. de Saxcé, showed that Hausdorff dimension of $\mathbf {Bad}_A(\epsilon )$ is less than the full dimension m for almost every A [Reference Lim, de Saxcé and ShapiraLSS19]. In fact, it was shown that one can associate to A a certain point $x_A$ in the space of unimodular lattices $\operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ such that if $x_A$ has no escape of mass on average for a certain diagonal flow (see §1.2 for more details), which is satisfied by almost every point, then the Hausdorff dimension of $\mathbf {Bad}_A(\epsilon )$ is less than m.

In this article, we provide an effective bound on the dimension in terms of $\epsilon $ and a certain Diophantine property of A as follows. We say that an $m\times n$ matrix A is $\textit {singular on average}$ if for any $\epsilon>0$ ,

$$ \begin{align*}\lim_{N\to\infty}\frac{1}{N}| \{&l{\kern-1pt}\in{\kern-1pt}\{1,\ldots,N\}:\text{ there exists } q{\kern-1pt}\in{\kern-1pt}\mathbb{Z}^n \ \text{such that} \\ &\langle Aq\rangle_{\mathbf{r}}{\kern-1pt}<{\kern-1pt}\epsilon 2^{-l} \ \textrm{and} \ 0{\kern-1pt}<{\kern-1pt}\|q\|_{\mathbf{s}}{\kern-1pt}<{\kern-1pt}2^l\}| {\kern-1pt}={\kern-1pt}1.\end{align*} $$

Theorem 1.2. For any $A\in M_{m,n}(\mathbb {R})$ which is not singular on average, there exists a constant $c(A)>0$ depending on A such that for any $\epsilon>0$ , $\dim _H \mathbf {Bad}_{A}(\epsilon )\leq m-c(A){\epsilon }/{\log (1/\epsilon )}.$

Here, the constant $c(A)$ , which depends on $\eta _A$ in Proposition 4.1 and H in equation (4.7), encodes the quantitative singularity on average.

However, the third author, together with Y. Bugeaud, D. H. Kim, and M. Rams, showed that in the one-dimensional case ( $m=n=1$ ), $\mathbf {Bad}_\alpha (\epsilon )$ has full Hausdorff dimension for some $\epsilon>0$ if and only if $\alpha \in \mathbb {R}$ is singular on average [Reference Bugeaud, Kim, Lim and RamsBKLR21]. We generalize this characterization to the general dimensional setting.

Theorem 1.3. Let $A\in M_{m,n}(\mathbb {R})$ be a matrix. Then the following are equivalent.

  1. (1) For some $\epsilon> 0$ , the set $\mathbf {Bad}_{A}(\epsilon )$ has full Hausdorff dimension.

  2. (2) A is singular on average.

Note that the implication (1) ${\implies}$ (2) of Theorem 1.3 follows from Theorem 1.2. The other direction will be shown in §6.

1.2 Idea of the proofs

We mainly use entropy rigidity in homogeneous dynamics, a principle that the measure of maximal entropy is invariant for a suitable group [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10]. The main tool in [Reference Lim, de Saxcé and ShapiraLSS19] is a relative version of entropy rigidity. In this article, we effectivize this phenomenon (Theorem 2.12) in terms of the static entropy and conditional measures. To use the effective version of the entropy rigidity, for each invariant measure, we construct a ‘well-behaved’ partition and a $\sigma $ -algebra, well-behaved in the sense that the ‘dynamical $\delta $ -boundary’ has a small measure which is controlled uniformly (see Definition 2.6 and Lemma 2.7). We then compare the associated dynamical entropy and the static entropy. Section 2 consists of these results in the general setting of real Lie groups as in [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10], which are of independent interest.

To describe the scheme of the proofs for main theorems, we consider a more specific homogeneous space as follows. For $d=m+n$ , let us denote by $\operatorname {ASL}_d(\mathbb {R})=\operatorname {SL}_d(\mathbb {R})\ltimes \mathbb {R}^d$ the set of area-preserving affine transformations and denote by $\operatorname {ASL}_d(\mathbb {Z})=\operatorname {SL}_d(\mathbb {Z})\ltimes \mathbb {Z}^d=\operatorname {Stab}_{\operatorname {ASL}_d(\mathbb {R})}(\mathbb {Z}^d)$ the stabilizer of the standard lattice $\mathbb {Z}^d$ . We view $\operatorname {ASL}_d(\mathbb {R})$ as a subgroup of $\operatorname {SL}_{d+1}(\mathbb {R})$ by $\operatorname {ASL}_d(\mathbb {R})=\{(\begin {smallmatrix} g & v\\ 0 & 1\\ \end {smallmatrix}): g\in \operatorname {SL}_d(\mathbb {R}), v\in \mathbb {R}^d\},$ and take a lift of the element $g\in \operatorname {SL}_d(\mathbb {R})$ to $\operatorname {ASL}_d(\mathbb {R})\subset \operatorname {SL}_{d+1}(\mathbb {R})$ by $g\longmapsto (\begin {smallmatrix} g & 0\\ 0 & 1\\ \end {smallmatrix}),$ denoted again by g. For given weights $\mathbf {r}\in \mathbb {R}^{m}_{>0}$ and $\mathbf {s}\in \mathbb {R}^{n}_{>0}$ , we consider the $1$ -parameter diagonal subgroup

$$ \begin{align*}\{a_t=\mathrm{diag} (e^{r_1t},\ldots,e^{r_mt},e^{-s_1t},\ldots,e^{-s_nt})\}_{t \in \mathbb R}\end{align*} $$

in $\operatorname {SL}_d(\mathbb {R})$ and let $a\overset {\operatorname {def}}{=} a_1$ be the time-one map of the diagonal flow $a_t$ . We consider

$$ \begin{align*}U=\left\{ \left(\begin{matrix} I_m & A & 0\\ 0 & I_n & 0\\ 0 & 0 & 1\\ \end{matrix}\right) :A\in M_{m,n}(\mathbb{R})\right\};\; \; W=\left\{ \left(\begin{matrix} I_m & 0 & b\\ 0 & I_n & 0\\ 0 & 0 & 1\\ \end{matrix}\right) :b\in \mathbb{R}^m\right\},\end{align*} $$

both of which are unstable horospherical subgroups in $\operatorname {ASL}_d(\mathbb {R})$ for a.

The homogeneous spaces $\operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ and $\operatorname {ASL}_d(\mathbb {R})/\operatorname {ASL}_d(\mathbb {Z})$ can be seen as the space of unimodular lattices and the space of unimodular grids, that is, unimodular lattices translated by a vector in $\mathbb {R}^d$ , respectively. We say that a point $x\in \operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ has $\delta $ -escape of mass on average (with respect to the diagonal flow $a_t$ ) if for any compact set Q in $\operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ ,

$$ \begin{align*}\liminf_{N\to\infty}\frac{1}{N}|\{\ell\in\{1,\ldots,N\}: a_\ell x\notin Q\}|\ge\delta.\end{align*} $$

A point $x \in X$ has no escape of mass on average if it does not have $\delta $ -escape of mass on average for any $\delta>0$ .

For $A\in M_{m,n}(\mathbb {R})$ and $(A,b)\in {M}_{m,n}(\mathbb {R}) \times \mathbb {R}^m$ , we associate points

$$ \begin{align*}x_A\overset{\operatorname{def}}{=} \left(\begin{matrix} I_m & A\\ 0 & I_n\\ \end{matrix}\right)\operatorname{SL}_d(\mathbb{Z})\quad \text{and}\quad y_{A,b}\overset{\operatorname{def}}{=} \left(\begin{matrix} I_m & A & -b\\ 0 & I_n & 0\\ 0 & 0 & 1\\ \end{matrix}\right)\operatorname{ASL}_d(\mathbb{Z}),\end{align*} $$

respectively. In [Reference Lim, de Saxcé and ShapiraLSS19], it was shown that $\dim _H \mathbf {Bad}_A(\epsilon )<m$ for all $\epsilon>0$ if $x_A$ is $\textit {heavy}$ , which is a condition equivalent to no escape of mass on average. Note that $x_A$ is heavy for almost every $A\in M_{m,n}(\mathbb {R})$ . However, we remark that A is singular on average if and only if the corresponding point $x_A$ has $1$ -escape of mass on average (with respect to the diagonal flow $a_t$ ) by Dani’s correspondence (see also [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17]).

Now we give the outline of the proofs for Theorems 1.1 and 1.2. From the Dani correspondence, we characterize the Diophantine property $(A,b)\in \mathbf {Bad}(\epsilon )$ by the dynamical property that the orbit $(a_t y_{A,b})_{t\geq 0}$ is eventually in some target $\mathcal {L}_\epsilon $ (see §3.2). Using this characterization, we construct a-invariant measures with large dynamical entropies relative to W and U (Propositions 4.1 and 5.4), which are related to the Hausdorff dimensions of $\mathbf {Bad}_A(\epsilon )$ and $\mathbf {Bad}^b(\epsilon )$ , respectively. Here, we use ‘well-behaved’ $\sigma $ -algebra constructed in Proposition 2.8. Then we associate the dynamical entropies with the static entropies (Lemma 2.10). Finally, we obtain effective upper bounds for the Hausdorff dimensions of $\mathbf {Bad}_A(\epsilon )$ and $\mathbf {Bad}^b(\epsilon )$ using an effective version of the variational principle (Theorem 2.12).

To treat $\mathbf {Bad}_A(\epsilon )$ and $\mathbf {Bad}^b(\epsilon )$ at the same time, we need to consider the entropy relative to an arbitrary expanding closed subgroup L normalized by a, which is more general than [Reference Lim, de Saxcé and ShapiraLSS19]; in [Reference Lim, de Saxcé and ShapiraLSS19], the special case $L=W$ whose orbits stay in the compact fiber of $\operatorname {ASL}_d(\mathbb {R})/\operatorname {ASL}_d(\mathbb {Z}) \to \operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ is considered.

For $\mathbf {Bad}_A(\epsilon )$ , we treat the case when $x_A$ has some escape of mass on average as well, whereas $x_A$ has no escape of mass on average in [Reference Lim, de Saxcé and ShapiraLSS19]. We need to consider $\mathcal {L}_\epsilon \subset \operatorname {ASL}_d(\mathbb {R})/\operatorname {ASL}_d(\mathbb {Z})$ , which is non-compact, whereas in [Reference Lim, de Saxcé and ShapiraLSS19], for heavy $x_A$ , it was enough to consider the set of fibers over a compact part of $\operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ . In the case of $\mathbf {Bad}^b(\epsilon )$ , as fixing b does not determine the amount of excursion in the cusp, we need an additional step (Proposition 5.3) to control the measure near the cusp allowing a small amount of escape of mass.

Another new feature of this article is the use of the effective equidistribution of expanding translates under the diagonal action on $\operatorname {ASL}_d(\mathbb {R})/\operatorname {ASL}_d(\mathbb {Z})$ and $\operatorname {SL}_d(\mathbb {R})/\Gamma _q$ , where $\Gamma _q$ is a congruence subgroup of $\operatorname {SL}_d(\mathbb {Z})$ , in the case of $\mathbf {Bad}^b(\epsilon )$ . The former result is proved by the second author in [Reference KimKim], and the latter result is a slight modification of [Reference Khalil and LuethiKM23].

Note that [Reference KimKim, Reference Khalil and LuethiKM23] hold in the weighted setting and the only reason we consider the unweighted setting for the Hausdorff dimension of $\mathbf {Bad}^b(\epsilon )$ is the covering estimate in Theorem 5.1 ([Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17, Theorem 1.5]).

The article is organized as follows. In §2, we introduce entropy, relative entropy, and a general setup. In this general setup, we construct a partition with a well-behaved ‘dynamical $\delta $ -boundary’ and a $\sigma $ -algebra in a quantitative sense. From this construction, we compare the dynamical entropy and the static entropy. Finally, we prove an effective version of the variational principle for relative entropy in the spirit of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, §7.55]. In §3, we introduce preliminaries for the proofs of dimension upper bounds including properties of dimensions with respect to quasi-metrics. We also reduce badly approximable properties to dynamical properties in the space of grids in $\mathbb {R}^{m+n}$ . In §§4 and 5, we construct a-invariant measures on $\operatorname {ASL}_d(\mathbb {R})/\operatorname {ASL}_d(\mathbb {Z})$ with large relative entropy and estimate dimension upper bounds of Theorems 1.2 and 1.1 using the effective variational principle. We conclude the paper with §6, characterizing the singular on average property in terms of best approximations and show the (2) ${\implies}$ (1) part in Theorem 1.3 using a modified version of the Bugeaud–Laurent sequence in [Reference Bugeaud and LaurentBL05].

2 Effective version of entropy rigidity

In this section, we will establish an effective version of entropy rigidity in [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, §7]. There have been effective uniqueness results along the line of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10] in various settings: [Reference PoloPol11] for toral automorphisms, [Reference KadyrovKad15] for hyperbolic maps on Riemannian manifolds, [ Reference RührRüh16 ] on p-adic homogeneous spaces, and [Reference KhayutinKha17] for a p-adic diagonal action in the S-arithmetic setting. However, in all of the above results as well as in [Reference Kim, Lim and PaulinKLP23], there exists a partition compatible with the given map or flow in the sense that images under the iteration have boundaries of small measure with respect to any invariant measure of interest.

In our setting of a diagonal action on a quotient of real Lie groups, one of the main technical difficulties is that there is no such partition for all the invariant measures we consider. We thus construct a partition $\mathcal {P}$ for each invariant measure $\mu $ and control the $\mu $ -measure of its ‘dynamical $\delta $ -boundary’ $E_\delta $ constructed out of images of thickenings of the boundary $\mathcal {P}$ . The value $\mu (E_\delta )$ is bounded above uniformly over the partition $\mathcal {P}$ and the measure $\mu $ . See Lemma 2.7.

2.1 Entropy and relative entropy

In this subsection, we recall the definitions of the entropy and the relative entropy for $\sigma $ -algebras which we use in the later sections. We refer the reader to [Reference Einsiedler, Lindenstrauss and WardELW, Chs. 1 and 2] for basic properties of the entropy.

Definition 2.1. Let $(X,\mathcal {B},\mu ,T)$ be a measure-preserving system on a Borel probability space, and let $\mathcal {A}, \mathcal {C} \subseteq \mathcal {B}$ be sub- $\sigma $ -algebras. Suppose that $\mathcal {C}$ is countably generated. Note that there exists an $\mathcal {A}$ -measurable conull set $X'\subset X$ and a system $\{\mu _x^{\mathcal {A}}|x\in X'\}$ of measures on X, referred to as conditional measures, given for instance by [Reference Einsiedler, Lindenstrauss and WardELW, Theorem 2.2]. The information function of $\mathcal {C}$ given $\mathcal {A}$ with respect to $\mu $ is defined by

$$ \begin{align*}I_\mu(\mathcal{C}|\mathcal{A})(x)=-\log\mu_x^{\mathcal{A}}([x]_{\mathcal{C}}),\end{align*} $$

where $[x]_{\mathcal {C}}$ is the atom of $\mathcal {C}$ containing x.

  1. (1) The conditional (static) entropy of $\mathcal {C}$ given $\mathcal {A}$ is defined by

    $$ \begin{align*}H_{\mu}(\mathcal{C}|\mathcal{A})\overset{\operatorname{def}}{=}\int_X I_\mu(\mathcal{C}|\mathcal{A})(x)\,d\mu(x),\end{align*} $$
    which is the average of the information function $\mathcal {C}$ given $\mathcal {A}$ . If the $\sigma $ -algebra $\mathcal {A}$ is trivial, then we denote by $H_{\mu }(\mathcal {C})=H_{\mu }(\mathcal {C}|\mathcal {A})$ , which is called the (static) entropy of $\mathcal {C}$ . Note that the entropy of the countable partition $\xi =\{A_1,A_2,\ldots \}$ of X is given by
    $$ \begin{align*}H_\mu(\xi)=H(\mu(A_1),\ldots)=-\sum_{i\ge 1}\mu(A_i)\log\mu(A_i)\in[0,\infty],\end{align*} $$
    where $0\log 0=0$ .
  2. (2) Let $\mathcal {A}\subseteq \mathcal {B}$ be a sub- $\sigma $ -algebra such that $T^{-1}\mathcal {A} = \mathcal {A}$ . For any countable partition $\xi $ of X, let

    $$ \begin{align*}h_\mu(T,\xi)\overset{\operatorname{def}}{=}\lim_{n\to\infty}\frac{1}{n}H_\mu (\xi_0^{n-1}) =\inf_{n\ge 1}\frac{1}{n}H_\mu(\xi_0^{n-1}),\end{align*} $$
    $$ \begin{align*}h_\mu(T,\xi|\mathcal{A})\overset{\operatorname{def}}{=}\lim_{n\to\infty}\frac{1}{n}H_\mu(\xi_0^{n-1}|\mathcal{A}) =\inf_{n\ge 1}\frac{1}{n}H_\mu(\xi_0^{n-1}|\mathcal{A}),\end{align*} $$
    where $\xi _0^{n-1}= \bigvee _{i=0}^{n-1}T^{-i}\xi $ . The (dynamical) entropy of T is
    $$ \begin{align*}h_\mu(T)\overset{\operatorname{def}}{=}\sup_{\xi:H_\mu(\xi)<\infty}h_\mu(T,\xi).\end{align*} $$
    The conditional (dynamical) entropy of T given $\mathcal {A}$ is
    $$ \begin{align*}h_\mu(T|\mathcal{A})\overset{\operatorname{def}}{=}\sup_{\xi:H_\mu(\xi)<\infty}h_\mu(T,\xi|\mathcal{A}).\end{align*} $$

2.2 General setup

Let G be a closed real linear group (or connected, simply connected real Lie group) and let $\Gamma <G$ be a lattice subgroup. We consider the quotient $Y=G/\Gamma $ with a G-invariant probability measure $m_Y$ and call it the Haar measure on Y. Let $d_G$ be a right invariant metric on G, which induces the metric $d_Y$ on the space $Y=G/\Gamma $ , which is locally isometric to G. Let $r_y$ be the maximal injectivity radius at $y\in Y$ , which is the supremum of $r>0$ such that the map $g\mapsto gy$ is an isometry from the open r-ball $B^G_r$ around the identity in G onto the open r-ball $B^Y_r(y)$ around $y\in Y$ . For any $r>0$ , we denote

$$ \begin{align*}Y(r)\overset{\operatorname{def}}{=}\{y \in Y : r_y \geq r\}.\end{align*} $$

It follows from the continuity of the injectivity radius that $Y(r)$ is compact. Since $\Gamma $ is a lattice, we may assume that

(2.1) $$ \begin{align} r_{\max} \overset{\operatorname{def}}{=} \inf\{r>0 : r_y \leq r \text{ for all } y \in Y\}\leq 1\end{align} $$

by rescaling the right invariant metric $d_G$ on G. It follows that for any $r>1$ , $Y(r) = \varnothing $ . For any closed subgroup $L<G$ , we consider the right invariant metric $d_L$ by restricting $d_G$ on L, and similarly denote by $B^L_r$ the open r-ball around the identity in L.

In this section, we fix an element $a\in G$ which is $\operatorname {Ad}$ -diagonalizable over $\mathbb {R}$ . Let

$$ \begin{align*}G^{+} = \{g\in G|a^k g a^{-k}\to \mathrm{id} \ \textrm{as} \ k\to -\infty\}\end{align*} $$

be the unstable horospherical subgroup associated to a (or equivalently the stable horospherical subgroup associated to $a^{-1}$ ), which is always a closed subgroup of G in our setting.

2.3 Construction of $a^{-1}$ -descending, subordinate algebra and its entropy properties

In this subsection, our goal is to strengthen the results of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, §7] for our quantitative purposes.

Definition 2.2. [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Definition 7.25]

Let $G^+ <G$ be the unstable horospherical subgroup associated to a. Let $\mu $ be an a-invariant measure on Y and $L<G^+$ be a closed subgroup normalized by a.

  1. (1) We say that a countably generated $\sigma $ -algebra $\mathcal {A}$ is subordinate to L (mod $\mu $ ) if for $\mu $ -almost every (a.e.) y, there exists $\delta> 0$ such that

    (2.2) $$ \begin{align} {B^{L}_\delta\cdot y \subset [y]_{\mathcal{A}} \subset B^{L}_{\delta^{-1}}\cdot y.} \end{align} $$
  2. (2) We say that $\mathcal {A}$ is $a^{-1}$ -descending if $(a^{-1})^{-1}\mathcal {A}= a\mathcal {A} \subseteq \mathcal {A}$ .

For each $L<G^+$ and a-invariant ergodic probability measure $\mu $ on Y, there exists a countably generated $\sigma $ -algebra $\mathcal {A}$ which is $a^{-1}$ -descending and subordinate to L [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Proposition 7.37]. We will prove that such a $\sigma $ -algebra can be constructed so that we also have an explicit upper bound of the measure of the set violating equation (2.2) for fixed $\delta>0$ . To prove an effective version of the variational principle later, we need this quantitative estimate independent of $\mu $ .

We first introduce some notation that will be used in this subsection. For a subset $B\subset Y$ and $\delta>0$ , we denote by $\partial _\delta B$ the $\delta $ -neighborhood of the boundary of B, that is,

$$ \begin{align*}\partial_\delta B\overset{\operatorname{def}}{=}\Big\{y\in Y: \inf_{z\in B}d_Y(y,z)+\inf_{z\notin B}d_Y(y,z)<\delta\Big\}.\end{align*} $$

We also define the neighborhood of the boundary of a countable partition $\mathcal {P}$ by $\partial _\delta \mathcal {P} \overset {\operatorname {def}}{=} \bigcup _{P\in \mathcal {P}}\partial _\delta P.$ We deal with the entropy with respect to $a^{-1}$ , and thus for a given partition (or a $\sigma $ -algebra) $\mathcal {P}$ of Y,

$$ \begin{align*}\mathcal{P}_{\ell}^{\ell'}\overset{\operatorname{def}}{=}\bigvee_{k=\ell}^{\ell'} a^{k}\mathcal{P}\end{align*} $$

for any extended integers $\ell \leq \ell '$ in $\mathbb {Z}\cup \{\pm \infty \}$ . We first construct a finite partition which has small measures on neighborhoods of the boundary. The following lemma is the main ingredient of the effectivization in this section. A key feature is that the measure estimate below is independent of $\mu $ .

Lemma 2.3. There exists a constant $0<c<{1}/{10}$ depending only on G such that the following holds. Let $\mu $ be a probability measure on Y. For any $0<r<1$ and any measurable subset $\Omega \subset Y(2r)$ , there exist a measurable subset $K\subset Y$ and a partition $\mathcal {P}=\{P_1,\ldots ,P_N\}$ of K such that:

  1. (1) $\Omega \subseteq K\subseteq B^G_{({11}/{10})r}\Omega $ ;

  2. (2) for each $1\leq i\leq N$ , there exists $z_i\in B^G_{{r}/{10}}\Omega $ such that

    $$ \begin{align*}B_{{r}/{5}}^G\cdot z_i\subseteq P_i\subseteq B_{r}^G\cdot z_i,\quad K=\bigcup_{i=1}^{N}B_{r}^G\cdot z_i;\end{align*} $$
  3. (3) for any $0<\delta <cr$ ,

    $$ \begin{align*}\mu(\partial_\delta\mathcal{P})\leq \bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}\mu(B^G_{({12}/{10})r}\Omega).\end{align*} $$

Proof. Choose a maximal $({9}/{10})r$ -separated set $\{y_1,\ldots ,y_N\}$ of $\Omega $ .

Claim. There exist a constant $0<c<{1}/{10}$ depending only on G, and $\{g_i\}_{i=1}^N \subset B^G_{{r}/{10}}$ such that for $z_i=g_iy_i$ and for any $0<\delta <cr$ ,

(2.3) $$ \begin{align} {\sum_i(\mu(\partial_\delta (B_r^G\cdot z_i))+\mu(\partial_\delta (B_{{r}/{2}}^G\cdot z_i)))\leq \bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}\mu(B^G_{({12}/{10})r}\Omega).} \end{align} $$

Proof of the claim

To prove this claim, we randomly choose each $g_i$ with the independent uniform distribution on $B^G_{{r}/{10}}$ . For $0<\delta <{r}/{10}$ fixed, we have

For any $y\in B_{({12}/{10})r}^G\Omega $ , the number of $y_i$ terms contained in $B_{({12}/{10})r}^G \cdot y$ is at most $({33}/{9})^{\dim G}$ since $B_{({9}/{20})r}^G \cdot y_i$ terms are disjoint and contained in $B_{({33}/{20})r}^G \cdot y$ . It implies that for any $y\in B_{({12}/{10})r}^G\Omega $ . It follows that

$$ \begin{align*}\mathbb{E}\bigg(\sum_i\mu(\partial_\delta (B_r^G\cdot z_i))\bigg)\ll \frac{\delta}{r}\int_{B_{({12}/{10})r}^G\Omega}4^{\dim G}\,d\mu(y)\ll \frac{\delta}{r}\mu(B_{({12}/{10})r}^G\Omega),\end{align*} $$

where the implied constant depends only on G.

Applying the same argument for $\partial _\delta (B_{{r}/{2}}^G\cdot z_i)$ instead of $\partial _\delta (B_r^G\cdot z_i)$ ,

$$ \begin{align*}\mathbb{E}\bigg(\sum_i(\mu(\partial_\delta (B_r^G\cdot z_i))+\mu(\partial_\delta (B_{{r}/{2}}^G\cdot z_i)))\bigg)\ll\frac{\delta}{r}\mu(B^G_{({12}/{10})r}\Omega).\end{align*} $$

It follows from Chebyshev’s inequality that

$$ \begin{align*}\mathbb{P}\bigg(\sum_i(\mu(\partial_\delta (B_r^G\cdot z_i))+\mu(\partial_\delta (B_{{r}/{2}}^G\cdot z_i)))\ge \frac{1}{2}\bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}\mu(B^G_{({12}/{10})r}\Omega)\bigg)\ll \bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}. \end{align*} $$

Hence, we have

(2.4) $$ \begin{align} \mathbb{P}&\bigg(\bigcap_{k\geq 0}\bigg\{\sum_i(\mu(\partial_{2^{-k}\delta} (B_r^G\cdot z_i))+\mu(\partial_{2^{-k}\delta} (B_{{r}/{2}}^G\cdot z_i)))< \frac{1}{2}\bigg(\frac{2^{-k}\delta}{r}\bigg)^{{1}/{2}}\mu(B^G_{({12}/{10})r}\Omega)\bigg\}\bigg)\nonumber\\&>1-O\bigg( \bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}\bigg). \end{align} $$

Thus, there exists $0<c<{1}/{10}$ so that the right-hand side of equation (2.4) is positive for any $\delta <cr$ . It follows that we can find $\{g_i\}_{i=1}^{N}$ such that the $z_i=g_iy_i$ terms satisfy equation (2.3) for any $0<\delta <cr$ .

Let $c>0$ and $\{g_i\}_{i=1}^N \subset B^G_{{r}/{10}}$ be as in the above claim. The set $\{z_i = g_i y_i\}_{i=1}^N$ is $({7}/{10})r$ -separated since $\{y_i\}_{i=1}^N$ is $({9}/{10})r$ -separated. Let $K\overset {\operatorname {def}}{=}\bigcup _{i=1}^N B_r^G\cdot z_i$ . Since $B_{({9}/{10})r}^G\cdot y_i\subseteq B_r^G\cdot z_i\subseteq B_{({11}/{10})r}^G\cdot y_i$ , we have

$$ \begin{align*}\Omega\subseteq\bigcup_{i=1}^N B_{({9}/{10})r}^G\cdot y_i\subseteq K\subseteq \bigcup_{i=1}^N B_{({11}/{10})r}^G\cdot y_i\subseteq B_{({11}/{10})r}^G \Omega.\end{align*} $$

Now we define a partition $\mathcal {P}$ of K inductively as follows:

$$ \begin{align*}P_i\overset{\operatorname{def}}{=} B^G_r\cdot z_i\setminus\bigg(\bigcup_{j=1}^{i-1}P_j\cup\bigcup_{j=i+1}^{N}B^G_{{r}/{2}}\cdot z_j\bigg)\end{align*} $$

for $1\leq i\leq N$ . By definition, we have $B_{{r}/{5}}^G\cdot z_i\subseteq P_i\subseteq B_{r}^G\cdot z_i$ and $z_i\in B^G_{{r}/{10}}\Omega $ for  $1\leq i\leq N$ . We also observe that the $\delta $ -neighborhood of $\mathcal {P}$ is contained in ${\bigcup _{i=1}^N (\partial _\delta (B_r^G\cdot z_i) \cup \partial _\delta (B_{{r}/{2}}^G\cdot z_i))}$ . Hence, it follows from the above claim that

$$ \begin{align*}\mu(\partial_\delta\mathcal{P})\leq \sum_i(\mu(\partial_\delta (B_r^G\cdot z_i))+\mu(\partial_\delta (B_{{r}/{2}}^G\cdot z_i)))\leq \bigg(\frac{\delta}{r}\bigg)^{{1}/{2}}\mu(B^G_{({12}/{10})r}\Omega)\end{align*} $$

for any $0<\delta <cr$ .

Remark 2.4. In Lemma 2.3, if $y \in \Omega $ is given and we let $y_1 = y \in \Omega $ in the proof, then ${y \in B_{{r}/{10}}^G \cdot z_1}$ , and thus $y \notin \partial \mathcal {P}$ , which will be used in the proofs of Propositions 4.1 and 5.4.

We need the following thickening properties. It can easily be checked that for any $r>\delta >0$ , we have

(2.5) $$ \begin{align} { B_{\delta}^G Y(r) \subset Y(r-\delta)\quad \text{and}\quad B_\delta^G Y(r)^c \subset Y(r+\delta)^c. } \end{align} $$

Using Lemma 2.3 inductively, we have the following partition of Y with its subpartition having small boundary measures. Recall that $Y(r) = \varnothing $ for any $r>1$ by equation (2.1).

Lemma 2.5. Let $0<r_0\leq 1$ be given and $\mu $ be a probability measure on Y. There exists a partition $\{K_k\}_{k=1}^{\infty }$ of Y such that for each $k\geq 1$ , the following statements hold:

  1. (1) $K_k\subseteq Y(2^{-k})\setminus Y(2^{-k+2});$

  2. (2) there exist a partition $\mathcal {P}_k=\{P_{k1},\ldots ,P_{kN_k}\}$ of $K_k$ and a point $z_i\in B^G_{({1}/{10})r_0 2^{-k-1}}K_k$ for each $1\leq i\leq N_k$ satisfying

    $$ \begin{align*}B_{({1}/{5})r_0 2^{-k-1}}^G\cdot z_i\subseteq P_{ki}\subseteq B_{r_0 2^{-k-1}}^G\cdot z_i;\end{align*} $$
  3. (3) $\mu (\partial _\delta \mathcal {P}_k)\leq (r_0^{-1} 2^{k+4}\delta )^{1/2}\mu (Y(2^{-k-1})\setminus Y(2^{-k+3}))$ for any $0<\delta <cr_02^{-k-2}$ , where $c>0$ is the constant in Lemma 2.3.

Proof. We will construct $\{K_k\}_{k\ge 1}$ and $\{\mathcal {P}_k\}_{k\ge 1}$ using Lemma 2.3 inductively. For each $k\geq 1$ , let us say that $K_k$ and $\mathcal {P}_k$ satisfy  $(\spadesuit _k)$ if they satisfy the three conditions in the statement. We will also need auxiliary bounded sets $K_k'\subset Y$ and corresponding partitions $\mathcal {P}_k'$ of $K_k'$ during the inductive process. Let us say that $K_k'$ and $\mathcal {P}_k'$ satisfy $(\clubsuit _k)$ if they satisfy the following three conditions:

  1. (1) $Y(2^{-k+1})\setminus \bigcup _{j=1}^{k-1}K_j\subseteq K_k'\subseteq B^G_{({11}/{10})r_02^{-k-1}}(Y(2^{-k+1}) \setminus \bigcup _{j=1}^{k-1}K_j)$ ;

  2. (2) for each $1\leq i\leq N_{k}$ , there exists $z_{ki}\in B^G_{({1}/{10})r_0 2^{-k-1}}K_k'$ such that

    $$ \begin{align*}B_{({1}/{5})r_0 2^{-k-1}}^G\cdot z_{ki}\subseteq P_{ki}'\subseteq B_{r_0 2^{-k-1}}^G\cdot z_{ki}\quad\text{and} \quad K_k'=\bigcup_{i=1}^{N}B_{r_02^{-k-1}}^G\cdot z_{ki};\end{align*} $$
  3. (3) $\mu (\partial _\delta \mathcal {P}_k')\leq (r_0^{-1}2^{k+1}\delta )^{1/2}\mu (Y(2^{-k})\setminus Y(2^{-k+3}))$ for any $0<\delta <cr_02^{-k-1}$ .

Here, $\bigcup _{j=1}^{0} K_j \overset {\operatorname {def}}{=} \emptyset $ .

We first choose $\Omega _1 = Y(1)$ and apply Lemma 2.3 with $r=r_02^{-2}$ and $\Omega =\Omega _1 \subset Y({r_0}/{2})$ . Then we have a subset $K_1' \subset Y$ and a partition $\mathcal {P}_1'$ of $K_1'$ satisfying conditions (1), (2) of $(\clubsuit _{1})$ , and

$$ \begin{align*}\mu(\partial_\delta\mathcal{P}_1')\leq (r_0^{-1} 2^2\delta)^{{1}/{2}}\mu(B_{({12}/{10})r_02^{-2}}^G \Omega_1)\end{align*} $$

for any $0<\delta <cr_02^{-2}$ . It follows from equation (2.5) that $B_{({12}/{10})r_02^{-2}}^G Y(1) \subset Y(\tfrac 12)$ , which implies condition (3) of $(\clubsuit _{1})$ since $Y(4)=\varnothing $ . Note that $K_1' \subset B_{({11}/{10})r_02^{-2}}^G Y(1)\subset Y(\tfrac 12)$ .

Now let $\Omega _2 = Y(\tfrac 12) \setminus K_1'$ and apply Lemma 2.3 again with $r=r_02^{-3}$ and $\Omega =\Omega _2 \subset Y({r_0}/{4})$ . We have a subset $K_2' \subset Y$ and a partition $\mathcal {P}_2'$ of $K_2'$ satisfying $\Omega _2 \subset K_2' \subset B_{({11}/{10})r_0 2^{-3}}^G \Omega _2$ , (2) of $(\clubsuit _{2})$ , and $\mu (\partial _\delta \mathcal {P}_2')\leq (r_0^{-1} 2^{3}\delta )^{1/2}\mu (B_{({12}/{10})r_0 2^{-3}}^G \Omega _2)$ for any $0<\delta <cr_02^{-3}$ . Setting $K_1 = K_1' \setminus K_2 '$ , condition (1) of $(\clubsuit _{2})$ and condition (1) of $(\spadesuit _1)$ follow since $Y(2)=\varnothing $ . Since $K_1' \supset Y(1)$ , it follows from equation (2.5) that $B_{({12}/{10})r_02^{-3}}^G \Omega _2 \subset Y(\tfrac 14)\setminus Y(2)$ , which implies condition (3) of $(\clubsuit _{2})$ .

Define a partition $\mathcal {P}_1{\kern-1pt} = {\kern-1pt}\{P_{11},\ldots , P_{1N_1}\}$ from $\mathcal {P}_1'{\kern-1pt} ={\kern-1pt} \{ P_{11}',\ldots , P_{1N_1}'\}$ by $P_{1i}{\kern-1pt}={\kern-1pt}P_{1i}' \setminus K_{2}'$ for each $1\leq i\leq N_1$ . For each $1\leq i\leq N_1$ and $y\in B^G_{(1/5)r_02^{-2}}\cdot z_{1i}$ , observe that $y\notin K_{2}'$ since $B^G_{r_02^{-2}}\cdot z_{1i}\subset K_1'$ and $K_{2}'\subset B^G_{({11}/{10})r_02^{-3}}\Omega _2 \subset B^G_{({11}/{10})r_0 2^{-3}}(Y\setminus K_1')$ . Hence, $B^G_{(1/5)r_02^{-2}}\cdot z_{1i}\subset P_{1i}$ holds, so condition (2) of $(\spadesuit _1)$ follows. Since $P_{1i}=P_{1i}' \setminus K_{2}'$ for each $1\leq i\leq N_1$ , we have

$$ \begin{align*} \mu(\partial_\delta \mathcal{P}_1) &\leq \mu(\partial_\delta \mathcal{P}_1') + \mu(\partial_\delta \mathcal{P}_2')\\ &\leq (r_0^{-1}2^2 \delta)^{{1}/{2}}\mu(Y(2^{-1})\setminus Y(2^2)) + (r_0^{-1}2^3 \delta)^{{1}/{2}}\mu(Y(2^{-2})\setminus Y(2)) \\ &\leq (r_0^{-1}2^5 \delta)^{{1}/{2}}\mu(Y(2^{-2})\setminus Y(2^2)) \end{align*} $$

for any $0<\delta <cr_02^{-3}$ . Hence, condition (3) of $(\spadesuit _1)$ follows.

Our desired disjoint sets $\{K_k\}_{k\ge 1}$ and partitions $\{\mathcal {P}_k\}_{k\ge 1}$ will be obtained by applying this process repeatedly.

Claim. For $k\ge 2$ , suppose that we have disjoint bounded sets $K_j$ of Y and corresponding partitions $\mathcal {P}_j$ satisfying $(\spadesuit _j)$ for $j=1,\ldots , k-1$ , and a subset $K_k' \subset Y$ and a partition $\mathcal {P}_k'$ satisfying $(\clubsuit _k)$ . Then we can find $K_k\subseteq K_k'$ and a partition $\mathcal {P}_k$ of $K_k$ satisfying $(\spadesuit _k)$ , and $K_{k+1}' \subset Y$ and a partition $\mathcal {P}_{k+1}'$ of $K_{k+1}'$ satisfying $(\clubsuit _{k+1})$ .

Proof of the claim

Note that $K_k' \subset B^G_{({11}/{10})r_02^{-k-1}}Y(2^{-k+1})\subset Y(2^{-k})$ and $K_j \subset Y(2^{-j}) \subset Y(2^{-k})$ for each $j=1,\ldots , k-1$ . Let $\Omega _{k+1}=Y(2^{-k})\setminus (\bigcup _{j=1}^{k-1} K_j\cup K_k')$ and apply Lemma 2.3 with $r=r_02^{-k-2}$ and $\Omega =\Omega _{k+1} \subset Y(r_02^{-k-1})$ . There exist $K_{k+1}'\subset Y$ and a partition $\mathcal {P}_{k+1}'=\{P_{(k+1)1}',\ldots ,P_{(k+1)N_{k+1}}'\}$ of $K_{k+1}'$ satisfying $\Omega _{k+1} \subset K_{k+1}' \subset B_{({11}/{10})r_02^{-k-2}}^G \Omega _{k+1}$ , condition (2) of $(\clubsuit _{k+1})$ , and $\mu (\partial _\delta \mathcal {P}_{k+1}')\leq (r_0^{-1}2^{k+2}\delta )^{1/2}\mu (B_{({12}/{10})r_02^{-k-2}}^G \Omega _{k+1})$ for any $0<\delta <cr_02^{-k-2}$ . Setting $K_k=K_k'\setminus K_{k+1}'$ , condition (1) of $(\clubsuit _{k+1})$ follows. Since $\bigcup _{j=1}^{k-1} K_j \supset Y(2^{-k+2})$ and $K_k \subset K_k' \subset Y(2^{-k}) \setminus \bigcup _{j=1}^{k-1}K_j$ , condition (1) of $(\spadesuit _k)$ follows. It follows from $\bigcup _{j=1}^{k-1} K_j \cup K_k' \supset Y(2^{-k+1})$ and equation (2.5) that

$$ \begin{align*}B_{({12}/{10})r_02^{-k-2}}^G \Omega_{k+1} \subset Y(2^{-k-1})\setminus Y(2^{-k+2}),\end{align*} $$

which implies condition (3) of $(\clubsuit _{k+1})$ . Define a partition $\mathcal {P}_k=\{P_{k1},\ldots , P_{kN_k}\}$ from $\mathcal {P}_k'=\{P_{k1}',\ldots ,P_{kN_k}'\}$ by $P_{ki}=P_{ki}'\setminus K_{k+1}'$ for any $1\leq i\leq N_k$ . For each $1\leq i\leq N_k$ and $y\in B^G_{(1/5)r_02^{-k-1}}\cdot z_{ki}$ , observe that $y\notin K_{k+1}'$ since $B^G_{r_02^{-k-1}}\cdot z_{ki}\subseteq K_k'$ and $K_{k+1}'\subseteq B^G_{({11}/{10})r_02^{-k-2}}\Omega _{k+1} \subset B^G_{({11}/{10})r_02^{-k-2}}(Y\setminus K_k')$ . Hence, $B^G_{(1/5)r_02^{-k-1}}\cdot z_{ki}\subset P_{ki}$ holds, so condition (2) of $(\spadesuit _k)$ follows. Since $P_{ki}=P_{ki}' \setminus K_{k+1}'$ for each $1\leq i\leq N_k$ , we have

$$ \begin{align*} \mu(\partial_\delta \mathcal{P}_k) &\leq \mu(\partial_\delta \mathcal{P}_k') + \mu(\partial_\delta \mathcal{P}_{k+1}') \\&\leq (r_0^{-1}2^{k+1}\delta)^{{1}/{2}}\mu(Y(2^{-k})\setminus Y(2^{-k+3})) + (r_0^{-1}2^{k+2}\delta)^{{1}/{2}}\mu(Y(2^{-k-1})\setminus Y(2^{-k+2})) \\&\leq (r_0^{-1}2^{k+4}\delta)^{{1}/{2}}\mu(Y(2^{-k-1})\setminus Y(2^{-k+3})) \end{align*} $$

for any $0<\delta <cr_02^{-k-2}$ . Hence, condition (3) of $(\spadesuit _k)$ follows.

The claim concludes the proof of Lemma 2.5.

By [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Lemmas 7.29 and 7.45], there are constants $\alpha>0$ and $d_0>0$ depending on a and G such that for every $r\in (0,1]$ ,

(2.6) $$ \begin{align} {a^{-k}B_{r}^{G^+}a^{k}\subset B_{d_0 e^{-k\alpha}r}^{G}} \end{align} $$

for any $k\in \mathbb {Z}$ . It implies that $a^{k}B_{r}^{G}a^{-k}\subset B_{d_0 e^{k\alpha }r}^{G}$ for $k\geq 0$ .

The following lemma is a quantitative strengthening of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Lemma 7.31]. We remark that the constants below are independent of $\mu $ and $\mathcal {P}$ while the ‘dynamical $\delta $ -boundary’ $E_\delta $ depends on $\mu $ .

Definition 2.6. We define the dynamical $\delta $ -boundary of the partition $\mathcal {P}$ by

$$ \begin{align*}E_\delta=\bigcup_{k=0}^\infty a^k\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}.\end{align*} $$

Lemma 2.7. Given $0<r_0\leq 1$ and an a-invariant probability measure $\mu $ on Y, let $\{K_j\}_{j\ge 1}$ and $\{\mathcal {P}_j\}_{j\ge 1}$ be the sets and the partitions in Lemma 2.5. Let $c>0$ and $d_0>0$ be the constants in Lemma 2.3 and equation (2.6), respectively. Let $\mathcal {P}$ be the countable partition $\mathcal {P}\overset {\operatorname {def}}{=}\bigcup _{j=1}^\infty \mathcal {P}_j$ of $Y.$

There exist $C_1,C_2>0$ , depending only on $r_0, a$ , and G, such that for any $0<\delta <\min (({cr_0}/{16d_0})^{2},1)$ , the dynamical $\delta $ -boundary $E_\delta \subset Y$ satisfies

$$ \begin{align*}\mu(E_{\delta})<\mu(Y\setminus Y(C_1\delta^{{1}/{2}}))+C_2\delta^{{1}/{4}}\end{align*} $$

and $B^{G^+}_{\delta }\cdot y\subset [y]_{\mathcal {P}_0^{\infty }}$ for any $y\in Y\setminus E_\delta $ .

Proof. We split $E_\delta $ into two subsets

$$ \begin{align*}&E_\delta'=\bigcup_{k=0}^\infty a^k\bigg(\bigcup_{i= 2+\lceil({\alpha}/{\log 2})k-({\log\delta})/({2\log2})\rceil}^\infty\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i\bigg) \quad \mathrm{and} \\ &E_\delta"=\bigcup_{k=0}^\infty a^k\bigg(\bigcup_{i= 1}^{1+\lceil({\alpha}/{\log 2})k-({\log\delta})/({2\log2})\rceil}\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i\bigg).\end{align*} $$

We first claim that $E_\delta '\subset Y\setminus Y((d_0+d_0^2)\delta ^{1/2})$ . Let $y\in E_\delta '$ , that is, $y\in a^k\partial _{d_0e^{-k\alpha }\delta }P$ for some $k\geq 0$ and $P\in \mathcal {P}_i$ for some $i \geq 2+\lceil ({\alpha }/{\log 2})k-({\log \delta })/({2\log 2})\rceil .$ By Lemma 2.5,

$$ \begin{align*}P \subset K_i \subset Y(2^{-i})\setminus Y(2^{-i+2}) \subset Y(2^{-i+2})^c.\end{align*} $$

It follows from equation (2.5) that

(2.7) $$ \begin{align} { \partial_{d_0e^{-k\alpha}\delta}P \subset B_{d_0e^{-k\alpha}\delta}^G P \subset B_{d_0e^{-k\alpha}\delta}^G Y(2^{-i+2})^c \subset Y(2^{-i+2}+d_0e^{-k\alpha}\delta)^c. } \end{align} $$

Using equation (2.6), for any $0<r<1, a^k Y(r)^c \subset Y(d_0e^{k\alpha }r)^c.$ Since $e^{k\alpha }2^{-i+2}\leq \delta ^{1/2}$ , combining with equation (2.7),

$$ \begin{align*} a^k\partial_{d_0e^{-k\alpha}\delta}P \subset a^k Y(2^{-i+2}+d_0e^{-k\alpha}\delta)^c \subset Y((d_0+d_0^2)\delta^{{1}/{2}})^c. \end{align*} $$

This proves the claim. It follows that

(2.8) $$ \begin{align} { \mu(E_\delta') \leq \mu(Y\setminus Y(C_1 \delta^{{1}/{2}})), } \end{align} $$

where $C_1=d_0 +d_0^{2}$ is a constant depending only on a and G.

Next we estimate $\mu (E_\delta ")$ . It follows from the a-invariance of $\mu $ that

(2.9) $$ \begin{align}\kern-4pt { \mu(E_\delta") \leq\sum_{k=0}^{\infty}\sum_{i=1}^{1+\lceil({\alpha}/{\log 2})k-({\log\delta})/({2\log2})\rceil} \mu(\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i)= \sum_{i=1}^{\infty}\sum_{k=k_i}^{\infty}\mu(\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i), } \end{align} $$

where $k_i\in \mathbb {N}$ denotes the smallest number of k such that $1+\lceil ({\alpha }/{\log 2})k-({\log \delta })/({2\log 2})\rceil \geq i$ . Note that $k_i\geq ({\log 2}/{\alpha })(i-2)+({\log \delta })/{2\alpha }$ .

However, by Lemma 2.5, we have

(2.10) $$ \begin{align} {\mu(\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i)\leq (r_0^{-1}2^{i+4}d_0e^{-k\alpha}\delta)^{{1}/{2}} \mu(Y(2^{-i-1})\setminus Y(2^{-i+3}))} \end{align} $$

for any $k\geq k_i$ , since $d_0e^{-k\alpha }\delta \leq d_02^{-i+2}\delta ^{1/2}<cr_02^{-i-2}$ . By equations (2.9) and (2.10), we have

(2.11) $$ \begin{align} \mu(E_\delta")&\leq \sum_{i=1}^{\infty}\sum_{k=k_i}^{\infty}\mu(\partial_{d_0e^{-k\alpha}\delta}\mathcal{P}_i) \leq \sum_{i=1}^{\infty}\sum_{k=k_i}^{\infty}(r_0^{-1}2^{i+4}d_0e^{-k\alpha}\delta)^{{1}/{2}} \mu(Y(2^{-i-1}){\kern-1pt}\setminus{\kern-1pt} Y(2^{-i+3})) \nonumber\\&=\sum_{i=1}^{\infty}(r_0^{-1}2^{i+4}e^{-k_i\alpha}\delta)^{{1}/{2}}(1-e^{-\alpha/2})^{-1}\mu(Y(2^{-i-1})\setminus Y(2^{-i+3}))\nonumber\\&\leq r_0^{-{1}/{2}}2^{3}\delta^{{1}/{4}}(1-e^{-\alpha/2})^{-1}\sum_{i=1}^{\infty}\mu(Y(2^{-i-1})\setminus Y(2^{-i+3}))\leq C_2 \delta^{{1}/{4}}, \end{align} $$

where $C_2 = 2^{5}r_0^{-1/2}(1-e^{-\alpha /2})^{-1}$ is a constant depending only on $r_0$ , a, and G. Combining equations (2.8) and (2.11), we finally have

$$ \begin{align*}\mu(E_\delta)<\mu(Y\setminus Y(C_1\delta^{{1}/{2}}))+C_2\delta^{{1}/{4}}\end{align*} $$

and the constants $C_1,C_2>0$ depend only on $r_0$ , a, and G.

It remains to check that $B^{G^+}_\delta \cdot y\subset [y]_{\mathcal {P}_0^\infty }$ for any $y\in Y\setminus E_{\delta }$ . Let $h\in B^{G^+}_{\delta }$ and suppose $[hy]_{\mathcal {P}_0^{\infty }}\neq [y]_{\mathcal {P}_0^{\infty }}$ . There is some $k\geq 0$ such that $a^{-k}hy$ and $a^{-k}y$ belong to different elements of the partition $\mathcal {P}$ . Since $a^{-k}ha^{k}\in a^{-k}B_{\delta }^{G^+}a^k \subset B_{d_0e^{-k\alpha }\delta }^G$ by equation (2.6), we have

$$ \begin{align*}d_Y (a^{-k}hy, a^{-k}y) \leq d_G (a^{-k}ha^{k},\mathrm{id}) \leq d_0e^{-k\alpha}\delta.\end{align*} $$

It follows that both $a^{-k}hy$ and $a^{-k}y$ belong to $\partial _{d_0e^{-k\alpha }\delta }\mathcal {P}$ , and hence $y\in E_\delta $ . It concludes that $B^{G^+}_{\delta }\cdot y\subset [y]_{\mathcal {P}_0^{\infty }}$ for any $y\in Y\setminus E_\delta $ .

The following proposition is a quantitative version of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Proposition 7.37]. Given a-invariant measure $\mu $ , the proposition provides a $\sigma $ -algebra which is $a^{-1}$ -descending and subordinate to L in the following quantitative sense.

Proposition 2.8. Let $0<r_0\leq 1$ be given, $\mu $ be an a-invariant probability measure on Y, and $L<G^+$ be a closed subgroup normalized by a. There exists a countably generated sub- $\sigma $ -algebra $\mathcal {A}^L$ of Borel $\sigma $ -algebra of Y satisfying:

  1. (1) $a\mathcal {A}^L \subset \mathcal {A}^L$ , that is, $\mathcal {A}^L$ is $a^{-1}$ -descending;

  2. (2) $[y]_{\mathcal {A}^L}\subset B_{r_02^{-k+1}}^{L}\cdot y$ for any $y\in Y(2^{-k})\setminus Y(2^{-k+2})$ with $k\geq 1$ ;

  3. (3) if $0<\delta <\min (({cr_0}/{16d_0})^{2},1)$ , then

    $$ \begin{align*}B_\delta^{L}\cdot y\subset [y]_{\mathcal{A}^L}\quad \mathrm{for\; any\;} y\in Y(\delta)\setminus E_\delta,\end{align*} $$
    where $c, d_0>0$ are the constants in Lemma 2.3 and equation (2.6), and $E_\delta $ is the dynamical $\delta $ -boundary defined in Lemma 2.7.

In particular, the $\sigma $ -algebra $\mathcal {A}^L$ is L-subordinate modulo $\mu $ .

Proof. For a given a-invariant probability measure $\mu $ on Y, let $\mathcal {P}$ be the countable partition of Y constructed in Lemma 2.7. We will construct a countably generated $\sigma $ -algebra $\mathcal {P}^L$ by taking L-plaques in each $P\in \mathcal {P}$ as atoms of $\mathcal {P}^L$ . Then, $\mathcal {A}^L\overset {\operatorname {def}}{=}(\mathcal {P}^L)_0^\infty $ will be the desired $\sigma $ -algebra.

For each $P\in \mathcal {P}$ , by Lemma 2.5, there exist $j\geq 1$ and $z\in P$ such that $P\in Y(2^{-j})\setminus Y(2^{-j+2})$ and $B_{(1/5)r_02^{-j-1}}^G \cdot z \subseteq P \subseteq B_{r_02^{-j-1}}^G \cdot z$ . We can find $B_P \subset G$ with $\operatorname {\mathrm {diam}}(B_P)\leq r_02^{-j}$ such that $P = \pi _Y (B_P)$ , where $\pi _Y : G \to Y$ is the natural quotient map. Let $\mathcal {B}_{G/L}$ be the Borel $\sigma $ -algebra of the quotient $G/L$ . Note that since L is closed, $\mathcal {B}_{G/L}$ is countably generated. Define the $\sigma $ -algebra

$$ \begin{align*}\mathcal{P}^L=\sigma(\{\pi_Y(B_P \cap S): P\in\mathcal{P},\ S\in \mathcal{B}_{G/L}\}).\end{align*} $$

Then, $\mathcal {P}^L$ is a refinement of $\mathcal {P}$ such that atoms of $\mathcal {P}^L$ are open L-plaques, that is, for any $y{\kern-1pt}\in{\kern-1pt} P {\kern-1pt}\in{\kern-1pt} \mathcal {P}$ , $[y]_{\mathcal {P}^L}{\kern-1pt}={\kern-1pt}[y]_{\mathcal {P}} \cap B_{r_02^{-j}}^L\cdot y{\kern-1pt}={\kern-1pt}V_y\cdot y$ , where $V_y{\kern-1pt}\subset{\kern-1pt} B_{r_02^{-j}}^L$ is an open bounded set.

It is clear that $\mathcal {P}^L$ is countably generated, and hence $\mathcal {A}^L=(\mathcal {P}^L)_0^\infty $ is also countably generated. By construction, we have $a\mathcal {A}^L=(\mathcal {P}^L)_1^\infty \subset \mathcal {A}^L$ , which proves the assertion (1).

For any $y\in Y(2^{-k})\setminus Y(2^{-k+2})$ with $k\geq 1$ , take $P\in \mathcal {P}$ such that $y\in P$ . By Lemma 2.5, there exist $j\geq 1$ and $z\in P$ such that $P\in Y(2^{-j})\setminus Y(2^{-j+2})$ and $P \subseteq B_{r_02^{-j-1}}^G \cdot z$ . Observe that $2^{-j+2}> 2^{-k}$ and $2^{-j} < 2^{-k+2}$ , that is, $j-2 < k < j+2 $ . Hence, we have

$$ \begin{align*}[y]_{\mathcal{A}^L}\subset [y]_{\mathcal{P}^L}=V_y\cdot y \subset B_{r_02^{-j}}^L\cdot y \subset B_{r_02^{-k+1}}^L \cdot y,\end{align*} $$

which proves the assertion (2).

For a given $0<\delta <\min (({cr_0}/{16d_0})^{2},1)$ and $y\in Y(\delta )\setminus E_\delta $ , assume that $z=hy$ with $h\in B_{\delta }^L$ . By Lemma 2.7, $B_\delta ^{G^+}\cdot y\subset [y]_{\mathcal {P}_0^\infty }$ . Hence, it follows that for any $k\geq 0$ , $a^{-k}y$ and $a^{-k}z$ belong to the same atom $P_k \subset \mathcal {P}$ . Then, we have

$$ \begin{align*} a^{-k}y,\; a^{-k}z=a^{-k}ha^{k}\cdot(a^{-k}y)\; \in P_k. \end{align*} $$

Note that for any $y\in Y(\delta )$ , the map $B_{\delta }^{G^+}\ni g \mapsto g y$ is injective, and hence the map $a^{-k}B_{\delta }^{G^+}a^k \ni g \mapsto ga^{-k}y$ is injective. Since $a^{-k}ha^{k} \in a^{-k}B_{\delta }^L a^k$ , $a^{-k}y$ and $a^{-k}z$ belong to the same atom of $\mathcal {P}^L$ . This proves the assertion (3).

As in [Reference Lim, de Saxcé and ShapiraLSS19, Lemma 3.4], we need to compare the dynamical entropy and the static entropy. In [Reference Lim, de Saxcé and ShapiraLSS19], the $\sigma $ -algebra $\pi ^{-1}(\mathcal {B}_X)$ is used to deal with the entropy relative to X, where $\mathcal {B}_X$ is the Borel $\sigma $ -algebra of X. To deal with the entropy relative to the general closed subgroup $L<G^+$ normalized by a, we consider the following tail $\sigma $ -algebra with respect to $\mathcal {A}^L$ in Proposition 2.8. Denote by

(2.12) $$ \begin{align} { \mathcal{A}_\infty^L \overset{\operatorname{def}}{=}\bigcap_{k=1}^{\infty}a^{k}\mathcal{A}^L= \bigcap_{k=1}^{\infty}(\mathcal{P}^L)_{k}^{\infty}. } \end{align} $$

This tail $\sigma $ -algebra may not be countably generated but it satisfies strict a-invariance, that is, $a\mathcal {A}_\infty ^L=\mathcal {A}_\infty ^L=a^{-1}\mathcal {A}_\infty ^L$ .

Lemma 2.9. Let $0<r_0\leq 1$ be given, $\mu $ be an a-invariant probability measure on Y, $L<G^+$ be a closed subgroup normalized by a, and $\mathcal {A}^L$ be as in Proposition 2.8. Then, the $\sigma $ -algebra $(\mathcal {A}^L)_{-\infty }^\infty $ is the Borel $\sigma $ -algebra of Y modulo $\mu $ .

Proof. Let $\mathcal {P}^L$ be as in the proof of Proposition 2.8. Since $(\mathcal {A}^L)_{-\infty }^\infty =(\mathcal {P}^L)_{-\infty }^{\infty }$ and $Y=\bigcup _{k\geq 1}Y(2^{-k})\setminus Y(2^{-k+2})$ , it is enough to show that for each $k\geq 1$ and for $\mu $ -a.e. $y\in Y(2^{-k})\setminus Y(2^{-k+2})$ , we have $[y]_{(\mathcal {P}^L)_{-\infty }^{\infty }}=\{y\}$ .

For fixed $k\geq 1$ , it follows from Poincaré recurrence (e.g. see [Reference Einsiedler and WardEW11, Theorem 2.11]) that for $\mu $ -a.e. $y\in Y(2^{-k})\setminus Y(2^{-k+2})$ , there exists an increasing sequence $(k_i)_{i\geq 1} \subset \mathbb {N}$ such that

$$ \begin{align*} a^{k_i}y\in Y(2^{-k})\setminus Y(2^{-k+2}) \quad \text{and}\quad k_i \to \infty\ \text{as}\ i\to \infty. \end{align*} $$

By Proposition 2.8(2), it follows that for each $i\geq 1$ ,

$$ \begin{align*}[a^{k_i}y]_{\mathcal{A}^L}=[a^{k_i}y]_{(\mathcal{P}^L)_0^{\infty}}\subset B_{r_02^{-k+1}}^L \cdot a^{k_i}y.\end{align*} $$

Since $[a^{k_i}y]_{(\mathcal {P}^L)_0^{\infty }}=a^{k_i}[y]_{a^{-k_i}(\mathcal {P}^L)_0^{\infty }}=a^{k_i}[y]_{(\mathcal {P}^L)_{-k_i}^{\infty }}$ , using equation (2.6), we have

$$ \begin{align*}[y]_{(\mathcal{P}^L)_{-k_i}^{\infty}} \subset a^{-k_i}B_{r_02^{-k+1}}^L \cdot a^{k_i}y =a^{-k_i}B_{r_02^{-k+1}}^L a^{k_i}\cdot y \subset B^L_{d_0e^{-\alpha k_i}r_02^{-k+1}}\cdot y.\end{align*} $$

Taking $i\to \infty $ , we conclude that $[y]_{(\mathcal {P}^L)_{-\infty }^{\infty }}=\{y\}$ .

Proposition 2.10. Let $0<r_0 \leq 1$ be given, $\mu $ be an a-invariant probability measure on Y, $L<G^+$ be a closed subgroup normalized by a, $\mathcal {A}^L$ be as in Proposition 2.8, and $\mathcal {A}^L_\infty $ be as in equation (2.12). Then, we have

(2.13) $$ \begin{align} {h_\mu(a|\mathcal{A}_\infty^L)=h_{\mu}(a^{-1}|\mathcal{A}_\infty^L) = H_{\mu}(\mathcal{A}^L|a\mathcal{A}^L).} \end{align} $$

Moreover, equation (2.13) holds for almost every ergodic component of $\mu $ .

Proof. Let $\mathcal {P}^L$ be as in the proof of Proposition 2.8. Since $\mathcal {P}^L$ is countably generated, we can take an increasing sequence of finite partitions $(\mathcal {P}_k^L)_{k\geq 1}$ of Y such that $\mathcal {P}_k^L \nearrow \mathcal {P}^L$ . By Lemma 2.9, we have $\mathcal {B}_Y=(\mathcal {P}^L)_{-\infty }^{\infty }=\bigvee _{k=1}^{\infty }(\mathcal {P}_k^L)_{-\infty }^{\infty }$ modulo $\mu $ , where $\mathcal {B}_Y$ is the Borel $\sigma $ -algebra of Y. It is clear that $(\mathcal {P}_k^L)_{-\infty }^{\infty }\subseteq (\mathcal {P}_{k+1}^L)_{-\infty }^{\infty }$ for all $k\in \mathbb {N}$ . Hence, it follow from Kolmogorov–Sinaı̆ theorem [Reference Einsiedler, Lindenstrauss and WardELW, Proposition 2.20] that

$$ \begin{align*} h_{\mu}(a^{-1}|\mathcal{A}_\infty^L)=\lim_{k\to\infty}h_{\mu}(a^{-1},\mathcal{P}_k^L|\mathcal{A}_\infty^L). \end{align*} $$

Using the future formula [Reference Einsiedler, Lindenstrauss and WardELW, Proposition 2.19(8)], we have

$$ \begin{align*} \lim_{k\to\infty}h_{\mu}(a^{-1},\mathcal{P}_k^L|\mathcal{A}_\infty^L)=\lim_{k\to\infty}H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L). \end{align*} $$

It follows from monotonicity and continuity of entropy [Reference Einsiedler, Lindenstrauss and WardELW, Propositions 2.10, 2.12, and 2.13] that for any fixed $k\geq 1$ ,

$$ \begin{align*} \lim_{\ell\to\infty}H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}_\ell^L)_1^\infty \vee \mathcal{A}_\infty^L)\leq H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L) \leq \lim_{\ell\to\infty}H_{\mu}(\mathcal{P}_\ell^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L), \end{align*} $$

and hence, we have

$$ \begin{align*} H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}^L)_1^\infty \vee \mathcal{A}_\infty^L)\leq H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L) \leq H_{\mu}(\mathcal{P}^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L). \end{align*} $$

Taking $k\to \infty $ , it follows that

$$ \begin{align*} \lim_{k\to\infty}H_{\mu}(\mathcal{P}_k^L|(\mathcal{P}_k^L)_1^\infty \vee \mathcal{A}_\infty^L) =H_{\mu}(\mathcal{P}^L|(\mathcal{P}^L)_1^\infty \vee \mathcal{A}_\infty^L)= H_{\mu}(\mathcal{A}^L|a\mathcal{A}^L), \end{align*} $$

which concludes equation (2.13).

Note that $\mathcal {B}_Y=(\mathcal {P}^L)_{-\infty }^{\infty }=\bigvee _{k=1}^{\infty }(\mathcal {P}_k^L)_{-\infty }^{\infty }$ modulo almost every ergodic component of $\mu $ . Thus, following the same argument as above, we can conclude equation (2.13) for almost every ergodic component of $\mu $ .

The quantity $H_\mu (\mathcal {A}^L|a\mathcal {A}^L)$ is called empirical entropy and is the average of the conditional information function

$$ \begin{align*}I_\mu(\mathcal{A}^L|a\mathcal{A}^L)(x)=-\log \mu_x^{a\mathcal{A}^L}([x]_{\mathcal{A}}),\end{align*} $$

and indeed the entropy contribution of L (see [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, 7.8] for definition).

2.4 Effective variational principle

This subsection is to effectivize the variational principle. We first recall the following ineffective variational principle. Combining [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Proposition 7.34] and [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Theorem 7.9], we have the following upper bound of an empirical entropy (or entropy contribution), and the entropy rigidity.

Theorem 2.11. [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10]

Let $L<G^{+}$ be a closed subgroup normalized by a and let $\mathfrak {l}$ denote the Lie algebra of L. Let $\mu $ be an a-invariant ergodic probability measure on Y. If $\mathcal {A}$ is a countably generated sub- $\sigma $ -algebra of the Borel $\sigma $ -algebra which is $a^{-1}$ -descending and L-subordinate, then

$$ \begin{align*}H_\mu(\mathcal{A}|a\mathcal{A})\leq \log\kern1.5pt\lvert\det(Ad_a|_{\mathfrak{l}})\rvert\end{align*} $$

and equality holds if and only if $\mu $ is L-invariant.

Let $L<G^+$ be a closed subgroup normalized by a, $m_L$ be the Haar measure on L, and $\mu $ be an a-invariant probability measure on Y. Let $\mathcal {A}$ be a countably generated sub- $\sigma $ -algebra of Borel $\sigma $ -algebra which is $a^{-1}$ -descending and L-subordinate modulo $\mu $ . Note that for any $j\in \mathbb {Z}_{\geq 0}$ , the sub- $\sigma $ -algebra $a^j \mathcal {A}$ is also countably generated, $a^{-1}$ -descending, and L-subordinate modulo $\mu $ .

For $y\in Y$ , denote by $V_y \subset L$ the shape of the $\mathcal {A}$ -atom at $y\in Y$ so that $V_y \cdot y=[y]_{\mathcal {A}}$ . It has positive $m_L$ -measure for $\mu $ -a.e. $y\in Y$ since $\mathcal {A}$ is L-subordinate modulo $\mu $ . Note that for any $j\in \mathbb {Z}_{\geq 0}$ , we have $[y]_{a^{j}\mathcal {A}}=a^{j}V_{a^{-j}y}a^{-j}\cdot y$ .

As in [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, 7.55] which is the proof of [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Theorem 7.9], let us define $\tau _{y}^{a^j \mathcal {A}}$ for $\mu $ -a.e $y\in Y$ to be the normalized push forward of $m_L|_{a^j V_{a^{-j}y}a^{-j}}$ under the orbit map, that is,

$$ \begin{align*} \tau_{y}^{a^j \mathcal{A}}=\frac{1}{m_L (a^j V_{a^{-j}y}a^{-j})}m_L|_{a^j V_{a^{-j}y}a^{-j}}\cdot y, \end{align*} $$

which is a probability measure on $[y]_{a^j\mathcal {A}}$ .

The following proposition is an effective version of Theorem 2.11.

Theorem 2.12. Let $L<G^+$ be a closed subgroup normalized by a and $\mu $ be an a-invariant ergodic probability measure on Y. Fix $j\in \mathbb {N}$ and denote by $J\ge 0$ the maximal entropy contribution of L for $a^j$ , that is,

$$ \begin{align*} J= \log\kern1.5pt\lvert\det(Ad_{a^j}|_{\mathfrak{l}})\rvert.\end{align*} $$

Let $\mathcal {A}$ be a countably generated sub- $\sigma $ -algebra of Borel $\sigma $ -algbera which is $a^{-1}$ -descending and L-subordinate. Suppose there exist a measurable subset $K\subset Y$ and a symmetric measurable subset $B\subset L$ such that $[y]_{\mathcal {A}}\subset B\cdot y$ for any $y\in K$ . Then, we have

$$ \begin{align*}H_\mu(\mathcal{A}|a^{j}\mathcal{A})\leq J+\int_Y \log\tau_y^{a^{j}\mathcal{A}}\big((Y\setminus K) \cup B\operatorname{Supp}\mu\big)\,d\mu(y).\end{align*} $$

Proof. By for instance [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, Theorem 5.9], for $\mu $ -a.e. $y\in Y$ , $\mu _y^{a^{j}\mathcal {A}}$ is a probability measure on $[y]_{a^{j}\mathcal {A}}=a^{j}V_{a^{-j}y}a^{-j}\cdot y$ , and $H_\mu (\mathcal {A}|a^{j}\mathcal {A})$ can be written as

$$ \begin{align*} {H_\mu(\mathcal{A}|a^{j}\mathcal{A})=-\int_Y\log \mu_y^{a^{j}\mathcal{A}}([y]_{\mathcal{A}})\,d\mu(y).} \end{align*} $$

Note that $m_L(a^jBa^{-j})=e^{J}m_L(B)$ for any measurable $B\subset L$ . Let

$$ \begin{align*}p(y)\overset{\operatorname{def}}{=} \mu_y^{a^{j}\mathcal{A}}([y]_{\mathcal{A}}) \quad\text{and}\quad p^{\mathrm{Haar}}(y)\overset{\operatorname{def}}{=} \tau_y^{a^{j}\mathcal{A}}([y]_{\mathcal{A}}).\end{align*} $$

Then, we have

$$ \begin{align*}p^{\mathrm{Haar}}(y)=\frac{m_L(V_y)}{m_L(a^jV_{a^{-j}y} a^{-j})}=\frac{m_L(V_y)}{m_L(V_{a^{-j}y})}e^{-J},\end{align*} $$

and hence, applying the ergodic theorem, we have $-\int _Y\log p^{\mathrm {Haar}}(y)\,d\mu (y)=J$ .

Now we estimate an upper bound of $H_\mu (\mathcal {A}|a^{j}\mathcal {A})-J$ following the computation in [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, 7.55]. Following [Reference Einsiedler, Lindenstrauss, Einsiedler, Ellwood, Eskin, Kleinbock, Lindenstrauss, Margulis, Marmi and YoccozEL10, 7.55], we can partition $[y]_{a^{j}\mathcal {A}}$ into a countable union of $\mathcal {A}$ -atoms as follows:

$$ \begin{align*}[y]_{a^{j}\mathcal{A}}=\bigcup_{i=1}^{\infty}[x_i]_{\mathcal{A}}\cup N_y,\end{align*} $$

where $N_y$ is a null set with respect to $\mu _y^{a^{j}\mathcal {A}}$ . Note that $\mu _y^{a^{j}\mathcal {A}}$ is supported on $\operatorname {Supp}\mu $ for $\mu $ -a.e y. Since $B\subset L$ is symmetric, if $x_i\in K\setminus B\operatorname {Supp}\mu $ , then $[x_i]_{\mathcal {A}}\subset B\cdot x_i\subset K\setminus \operatorname {Supp}\mu $ , and hence we have $\mu ^{a^{j}\mathcal {A}}_y([x_i]_{\mathcal {A}})=0$ . If $x_i \in (Y\setminus K) \cup B\operatorname {Supp}\mu $ and , then there exists $x_i' \in [x_i]_{\mathcal {A}}$ such that $x_i' \in K\setminus B\operatorname {Supp}\mu $ , and hence $\mu ^{a^{j}\mathcal {A}}_y([x_i]_{\mathcal {A}})=\mu ^{a^{j}\mathcal {A}}_y([x_i']_{\mathcal {A}})=0$ . Thus, we denote by Z the set of $x_i$ terms in $(Y\setminus K) \cup B\operatorname {Supp}\mu $ such that $[x_i]_{\mathcal {A}} \subset (Y\setminus K)\cup B\operatorname {Supp}\mu $ . It follows that

$$ \begin{align*} H_{\mu}(\mathcal{A}|a^j \mathcal{A})-J &= -\int_Y (\log p(z) - \log p^{\mathrm{Haar}}(z) )\,d\mu(z)\\&= \int_Y \int_Y (\log p^{\mathrm{Haar}}(z) - \log p(z) )\,d\mu_y^{a^j \mathcal{A}}(z)\,d\mu(y)\\&= \int_Y \sum_{x_i \in Z} \int_{z\in [x_i]_{\mathcal{A}}} (\log p^{\mathrm{Haar}}(z) - \log p(z) )\,d\mu_y^{a^j \mathcal{A}}(z)\,d\mu(y)\\&= \int_Y \sum_{x_i \in Z} \log\bigg(\frac{\tau^{a^{j}\mathcal{A}}_y([x_i]_{\mathcal{A}})}{\mu^{a^{j}\mathcal{A}}_y([x_i]_{\mathcal{A}})}\bigg)\mu^{a^{j}\mathcal{A}}_y([x_i]_{\mathcal{A}})\,d\mu(y)\\&\leq \int_Y \log\bigg(\sum_{x_i\in Z}\tau_y^{a^{j}\mathcal{A}}([x_i]_{\mathcal{A}})\bigg) \,d\mu(y)\\&\leq \int_Y \log \tau_y^{a^{j}\mathcal{A}}((Y\setminus K) \cup B \operatorname{Supp}\mu) \,d\mu(y). \end{align*} $$

The second last inequality follows from the convexity of the logarithm. This proves the proposition.

In particular, if $\mathcal {A}$ is of the form $a^{k}\mathcal {A}^L$ for $k\in \mathbb {Z}$ , then Theorem 2.12 still holds without assuming the ergodicity of $\mu $ .

Corollary 2.13. Let $0<r_0 \leq 1$ be given, $\mu $ be an a-invariant probability measure on Y, $L<G^+$ be a closed subgroup normalized by a, and $\mathcal {A}^L$ be as in Proposition 2.8. Then, Theorem 2.12 holds for $\mathcal {A}$ of the form $a^{k}\mathcal {A}^L$ for $k\in \mathbb {Z}$ .

Proof. Writing the ergodic decomposition $\mu =\int \mu _{z}^{\mathcal {E}}\,d\mu (z)$ , we have

$$ \begin{align*}h_\mu(a^j|\mathcal{A}^L_\infty)=\int h_{\mu_z^{\mathcal{E}}}(a^j|\mathcal{A}^L_\infty)\,d\mu(z),\end{align*} $$

where $\mathcal {A}^L_\infty $ is the $\sigma $ -algebra as in equation (2.12). By Proposition 2.10, we also have

$$ \begin{align*}H_\mu(\mathcal{A}^L|a^j\mathcal{A}^L)=\int H_{\mu_z^{\mathcal{E}}}(\mathcal{A}^L|a^j\mathcal{A}^L)\,d\mu(z).\end{align*} $$

It follows from the a-invariance of $\mu $ and $\mu _z^{\mathcal {E}}$ that

$$ \begin{align*}H_\mu(\mathcal{A}|a^j\mathcal{A})=\int H_{\mu_z^{\mathcal{E}}}(\mathcal{A}|a^j\mathcal{A})\,d\mu(z).\end{align*} $$

Applying Theorem 2.12 for each $\mu _{z}^{\mathcal {E}}$ , we obtain

$$ \begin{align*} H_\mu(\mathcal{A}|a^j\mathcal{A}) &= \int H_{\mu_z^{\mathcal{E}}}(\mathcal{A}|a^j\mathcal{A})\,d\mu(z)\\ &\leq J+\int_Y\int_Y \log\tau_y^{a^{j}\mathcal{A}}(B^2\operatorname{Supp}\mu_z^{\mathcal{E}})\,d\mu_z^{\mathcal{E}}(y)\,d\mu(z)\\ &\leq J+\int_Y \log\tau_y^{a^{j}\mathcal{A}}(B^2\operatorname{Supp}\mu)\,d\mu(y).\\[-3.4pc] \end{align*} $$

3 Preliminaries for the upper bound

From now on, we fix the following notation:

$$ \begin{align*}d=m+n,\; G=\operatorname{ASL}_d(\mathbb{R}),\; \Gamma=\operatorname{ASL}_d(\mathbb{Z}),\quad \text{and}\quad Y=G/\Gamma.\end{align*} $$

We use all notation in §2.2 with this setting. In particular, we choose a right invariant metric $d_G$ on G so that $r_{max} \leq 1$ . Denote by $d_\infty $ the metric on G induced from the max norm on $M_{d+1,d+1}(\mathbb {R})$ . Since $d_G$ and $d_\infty $ are locally bi-Lipschitz, there are constants $0<r_0<1$ and $C_0 \geq 1$ such that for any $x,y\in B_{r_0}^G$ ,

(3.1) $$ \begin{align} { \frac{1}{C_0} d_\infty (x,y) \leq d_{G}(x,y)\leq C_0 d_\infty (x,y). } \end{align} $$

Note that $r_0$ and $C_0$ depend only on G. In the rest of the article, all the statements from Lemma 2.5 to Proposition 2.10 will be applied to this $r_0$ .

Recall the notation $a_t$ , $a=a_1$ , U, and W in the introduction. The subgroups U and W are closed subgroups in $G^+$ normalized by a, where $G^+$ is the unstable horospherical subgroup associated to a. Denote by $\mathfrak {u}$ and $\mathfrak {w}$ the Lie algebras of U and W, respectively. We now consider the following quasinorms on $\mathfrak {u}=\mathbb {R}^{mn}=M_{m,n}(\mathbb {R})$ and $\mathfrak {w}= \mathbb {R}^{m}$ : For $A\in M_{m,n}(\mathbb {R})$ and $b\in \mathbb {R}^m$ , define

$$ \begin{align*}\|A\|_{\mathbf{r}\otimes\mathbf{s}}=\max_{\substack{1\leq i\leq m\\1\leq j\leq n}} |A_{ij}|^{{1}/({r_i +s_j})}\quad \text{and}\quad \|b\|_{\mathbf{r}}=\max_{1\leq i\leq m} |b_i|^{{1}/{r_i}}.\end{align*} $$

We call these quasinorms $\mathbf {r}\otimes \mathbf {s}$ -quasinorm and $\mathbf {r}$ -quasinorm, respectively.

We remark that for $A, A' \in M_{m,n}(\mathbb {R})$ and $b, b' \in \mathbb {R}^m$ , using the convexity of functions $s\mapsto s^{{1}/({r_i + s_j})}$ and $s\mapsto s^{{1}/{r_i}}$ ,

(3.2) $$ \begin{align} \begin{aligned}\|A+A'\|_{\mathbf{r}\otimes\mathbf{s}} &\leq 2^{({1-(r_m+s_n)})/({r_m +s_n})} (\|A\|_{\mathbf{r}\otimes\mathbf{s}}+\|A'\|_{\mathbf{r}\otimes\mathbf{s}});\\ \|b+b'\|_{\mathbf{r}} &\leq 2^{({1-r_m})/{r_m}} (\|b\|_{\mathbf{r}}+\|b'\|_{\mathbf{r}}). \end{aligned}\end{align} $$

It also holds that

$$ \begin{align*}\|\!\operatorname{Ad}_{a_t} A\|_{\mathbf{r}\otimes\mathbf{s}}=e^t\|A\|_{\mathbf{r}\otimes\mathbf{s}}\quad\text{and} \quad \|\operatorname{Ad}_{a_t} b\|_{\mathbf{r}}=e^t\|b\|_{\mathbf{r}}\end{align*} $$

for any $A\in M_{m,n}(\mathbb {R})$ and $b\in \mathbb {R}^m$ .

By a quasi-metric on a space Z, we mean a map $d_Z: Z \times Z \to \mathbb R_{\geq 0}$ which is a symmetric, positive definite map such that for some constant C, for all $x,y\in Z$ , $d_Z (x,y)\leq C(d_Z (x,z)+d_Z(z,y))$ . The $\mathbf {r}\otimes \mathbf {s}$ -quasinorm (respectively $\mathbf {r}$ -quasinorm) induces the quasi-metric $d_{\mathbf {r}\otimes \mathbf {s}}$ (respectively $d_{\mathbf {r}}$ ) on $\mathfrak {u}$ (respectively $\mathfrak {w}$ ). Note that the logarithm map is defined on U and W, and hence the quasi-metric $d_{\mathbf {r}\otimes \mathbf {s}}$ (respectively $d_{\mathbf {r}}$ ) induces the quasi-metric on U (respectively W) via the logarithm map. For simplicity, we keep the notation $d_{\mathbf {r}\otimes \mathbf {s}}$ and $d_{\mathbf {r}}$ for the quasi-metrics on U and W, respectively. We similarly denote by $B^{U,\mathbf {r}\otimes \mathbf {s}}_r$ (respectively $B^{W,\mathbf {r}}_r$ ) the open r-ball around the identity in U (respectively W) with respect to the quasi-metric $d_{\mathbf {r}\otimes \mathbf {s}}$ (respectively $d_{\mathbf {r}}$ ). For any $y\in Y$ , we also denote by $d_{\mathbf {r}\otimes \mathbf {s}}$ (respectively $d_{\mathbf {r}}$ ) the induced quasi-metric on the fiber $B_{r_y}^U\cdot y$ (respectively $B_{r_y}^W\cdot y$ ).

As in Theorem 2.11, we can explicitly compute the maximum entropy contributions for $L=U$ and $L=W$ . For $L=U$ , the restricted adjoint map is the expansion $\operatorname {Ad}_a:(A_{ij})\mapsto (e^{r_i+s_j}A_{ij})$ of $A\in M_{m,n}(\mathbb {R})$ , and hence

$$ \begin{align*} {\log\kern1.5pt\lvert\det(Ad_a|_{\mathfrak{u}})\rvert=\sum_{i=1}^m \sum_{j=1}^n (r_i+s_j)=m+n.} \end{align*} $$

For $L=W$ , the restricted adjoint map is the expansion $Ad_a:(b_i)\mapsto (e^{r_i}b_{i})$ of $b\in \mathbb {R}^m$ , and hence

$$ \begin{align*} {\log\kern1.5pt\lvert\det(Ad_a|_{\mathfrak{w}})\rvert=\sum_{i=1}^m r_i=1.} \end{align*} $$

Denote by $X=\operatorname {SL}_d(\mathbb {R})/\operatorname {SL}_d(\mathbb {Z})$ and by $\pi :Y\to X$ the natural projection sending a translated lattice $x +v$ to the lattice x. Equivalently, it is defined by $\pi ( (\begin {smallmatrix} g & v\\ 0 & 1\\ \end {smallmatrix})\Gamma ) =g \operatorname {SL}_d(\mathbb {Z})$ for $g\in \operatorname {SL}_d(\mathbb {R})$ and $v\in \mathbb {R}^d.$ We also use the following notation: $ w(v)=(\begin {smallmatrix} I_d & v\\ 0 & 1\\ \end {smallmatrix})$ for $v\in \mathbb {R}^d$ .

3.1 Dimensions

Let Z be a space endowed with a quasi-metric $d_Z$ . For a bounded subset $S\subset Z$ , the lower Minkowski dimension $\underline {\dim }_{d_Z} S$ with respect to the quasi-metric $d_Z$ is defined by

$$ \begin{align*} \underline{\dim}_{d_Z} S \overset{\operatorname{def}}{=} \liminf_{\delta\to 0} \frac{\log N_{\delta}(S)}{\log1/\delta},\end{align*} $$

where $N_{\delta }(S)$ is the maximal cardinality of a $\delta $ -separated subset of S for $d_Z$ .

Now, for subsets $S \subset \mathfrak {u}=\mathbb {R}^{mn}$ and $S' \subset \mathfrak {w}=\mathbb {R}^m$ in the Lie algebras $\mathfrak {u}$ and $\mathfrak {w}$ , we denote the lower Minkowski dimensions of these subsets as follows:

$$ \begin{align*}\underline{\dim}_{\mathbf{r}\otimes\mathbf{s}} S \overset{\operatorname{def}}{=} \underline{\dim}_{d_{\mathbf{r}\otimes\mathbf{s}}} S, \quad \underline{\dim}_{\mathbf{r}} S' \overset{\operatorname{def}}{=} \underline{\dim}_{d_{\mathbf{r}}} S'.\end{align*} $$

We will also consider Hausdorff dimensions $\dim _H S$ and $\dim _{H} S'$ , always defined with respect to the standard metric.

Lemma 3.1. [Reference Lim, de Saxcé and ShapiraLSS19, Lemma 2.2]

For subsets $S \subset \mathfrak {u}$ and $S' \subset \mathfrak {w}$ :

  1. (1) $\underline {\dim }_{\mathbf {r}\otimes \mathbf {s}} \mathfrak {u} = \sum _{i,j}(r_{i}+s_{j}) = m+n$ and $\underline {\dim }_{\mathbf {r}} \mathfrak {w} = \sum _{i}r_{i}=1$ ;

  2. (2) $\underline {\dim }_{\mathbf {r}\otimes \mathbf {s}} S \geq (m+n) - (r_1 +s_1)(mn - \dim _H S)$ ;

  3. (3) $\underline {\dim }_{\mathbf {r}} S' \geq 1 - r_1 (m-\dim _H S' )$ .

3.2 Correspondence with dynamics

For $y=(\begin {smallmatrix} g & v \\ 0 & 1 \\ \end {smallmatrix})\Gamma \in Y$ with $g\in \operatorname {SL}_d(\mathbb {R})$ and $v\in \mathbb {R}^d$ , denote by $\Lambda _y$ the corresponding unimodular grid $g\mathbb {Z}^d + v$ in $\mathbb {R}^d$ . We denote the $(\mathbf {r},\mathbf {s})$ -quasinorm of $v=(\mathbf {x},\mathbf {y})\in \mathbb {R}^m\times \mathbb {R}^n$ by $\|v\|_{\mathbf {r},\mathbf {s}}=\max \{\|\mathbf {x}\|_{\mathbf {r}}^{{d}/{m}},\|\mathbf {y}\|_{\mathbf {s}}^{{d}/{n}}\}$ . Let

$$ \begin{align*}\mathcal{L}_\epsilon\overset{\operatorname{def}}{=}\{y\in Y : \text{ for all } v\in\Lambda_y, \|v\|_{\mathbf{r},\mathbf{s}}\geq\epsilon\},\end{align*} $$

which is a (non-compact) closed subset of Y. Following [Reference KleinbockKle99, §1.3], we say that the pair $(A,b)\in M_{m,n}(\mathbb {R})\times \mathbb {R}^m$ is rational if there exists some $(p,q) \in \mathbb {Z}^m\times \mathbb {Z}^n$ such that $Aq-b+p=0$ , and irrational otherwise.

Proposition 3.2. For any irrational pair $(A,b)\in M_{m,n}(\mathbb {R})\times \mathbb {R}^m$ , $(A,b)\in \mathbf {Bad}(\epsilon )$ if and only if the $a_t$ -orbit of the point $y_{A,b}$ is eventually in $\mathcal {L}_\epsilon $ , that is, there exists $T\ge 0$ such that $a_t y_{A,b}\in \mathcal {L}_\epsilon $ for all $t\ge T$ .

Proof. Suppose that there exist arbitrarily large t terms satisfying $a_t y_{A,b}\notin \mathcal {L}_\epsilon $ . Denote $e^{\mathbf {r} t}\overset {\operatorname {def}}{=} \textrm {diag}(e^{r_1t},\ldots ,e^{r_mt})\in M_{m,m}(\mathbb {R})$ and $e^{\mathbf {s}t}\overset {\operatorname {def}}{=}\textrm {diag}(e^{s_1t},\ldots ,e^{s_nt})\in M_{n,n}(\mathbb {R}).$ Then, the vectors in the grid $\Lambda _{a_t y_{A,b}}$ can be represented as

$$ \begin{align*}a_t \left(\left(\begin{matrix} I_m & A \\ 0 & I_n \\ \end{matrix}\right) \left(\begin{matrix} p \\ q \\ \end{matrix}\right) +\left(\begin{matrix} -b \\ 0 \\ \end{matrix}\right)\right) =\left(\begin{matrix} e^{\mathbf{r} t}(Aq+p-b)\\ e^{-\mathbf{s} t}q\\ \end{matrix}\right)\end{align*} $$

for $(p,q)\in \mathbb {Z}^m\times \mathbb {Z}^n$ . Therefore, $a_t x_{A,b}\notin \mathcal {L}_\epsilon $ implies that for some $q\in \mathbb {Z}^n$ ,

(3.3) $$ \begin{align} {e^{t}\langle Aq-b\rangle_{\mathbf{r}}<\epsilon^{{m}/{d}} \quad \text{and}\quad e^{-t}\|q\|_{\mathbf{s}}<\epsilon^{{n}/{d}},} \end{align} $$

and thus $\|q\|_{\mathbf {s}}\langle Aq-b\rangle _{\mathbf {r}}<\epsilon $ . Since $\langle Aq-b\rangle _{\mathbf {r}}\neq 0$ for all q, we use the condition $\langle Aq-b\rangle _{\mathbf {r}}<e^{-t}\epsilon ^{{m}/{d}}$ for arbitrarily large t to conclude that $\|q\|_{\mathbf {s}}\langle Aq-b\rangle _{\mathbf {r}}<\epsilon $ holds for infinitely many q terms. This is a contradiction to the assumption that $(A,b)\in \mathbf {Bad}(\epsilon )$ .

However, if $(A,b)\notin \mathbf {Bad}(\epsilon )$ , then since $(A,b)$ is irrational, there are infinitely many $q\in \mathbb {Z}^n$ such that $\|q\|_{\mathbf {s}}\langle Aq-b\rangle _{\mathbf {r}}<\epsilon $ . Thus, we can choose arbitrarily large t so that equation (3.3) holds, which contradicts the assumption that the $a_t$ -orbit of the point $y_{A,b}$ is eventually in $\mathcal {L}_\epsilon $ .

Remark 3.3. We claim that for a fixed $b\in \mathbb {R}^m$ , the subset $\mathbf {Bad}_{0}^b(\epsilon )$ of rational $(A,b)$ terms in $\mathbf {Bad}^b(\epsilon )$ is a subset of $\mathbf {Bad}^0(\epsilon ).$ Indeed, if $A\in \mathbf {Bad}^b(\epsilon )$ for some b and $(A,b)$ is rational, then $\langle Aq_0-b\rangle _{\mathbf {r}}=0$ for some $q_0\in \mathbb {Z}^m$ and $\liminf _{\|q\|_{\mathbf {s}}\to \infty } \|q\|_{\mathbf {s}}\langle Aq-b\rangle _{\mathbf {r}}\ge \epsilon $ , and thus $\liminf _{\|q\|_{\mathbf {s}}\to \infty } \|q\|_{\mathbf {s}}\langle A(q-q_0)\rangle _{\mathbf {r}}\ge \epsilon $ . Therefore, we have

$$ \begin{align*}\dim_H\mathbf{Bad}_{0}^{b}(\epsilon)\leq\dim_H\mathbf{Bad}^0(\epsilon) =mn-c_{m,n}\frac{\epsilon}{\log 1/\epsilon}<mn\end{align*} $$

for some constant $c_{m,n}>0$ [Reference Kleinbock and MirzadehKM19]. For a fixed $A\in M_{m,n}(\mathbb {R})$ , the subset of $\mathbf {Bad}_A(\epsilon )$ such that $(A,b)$ is rational is of the form $Aq+p$ for some $q,p\in \mathbb {Z}^m$ and thus has Hausdorff dimension zero.

In the rest of the article, we will focus on the elements $y_{A,b}$ that are eventually in $\mathcal {L}_\epsilon $ .

3.3 Covering counting lemma

To construct measures of large entropy in Proposition 4.1 and 5.4, we will need the following counting lemma, which is a generalization of [Reference Lim, de Saxcé and ShapiraLSS19, Lemma 2.4].

Here, we consider two cases: $L=U$ and $L=W$ . Denote by $\mathbf {c}=(c_1,\ldots ,c_{\dim \mathfrak {l}})$ either $\mathbf {r}\otimes \mathbf {s}$ (for $L=U$ ) or $\mathbf {r}$ (for $L=U$ ), and denote by $\|\cdot \|_{\mathbf {c}}$ either $\|\cdot \|_{\mathbf {r}\otimes \mathbf {s}}$ (for $L=U$ ) or $\|\cdot \|_{\mathbf {r}}$ (for $L=W$ ). Let $J_L$ be the maximal entropy contribution for L. Recall that $J_U=m+n$ and $J_W=1$ .

Before stating the main result of this subsection, we fix the following notation. Fix a ‘cusp part’ $Q_\infty ^0\subset X$ that is a connected subset such that $X\smallsetminus Q_\infty ^0$ has compact closure. Set $Q_{\infty } = \pi ^{-1}(Q_\infty ^0)$ and denote by $r(Q_\infty )>0$ the infimum of injectivity radius on $Y\smallsetminus Q_\infty $ . For any $D>J_L$ , choose large enough $T_{D}\in \mathbb {N}$ such that for all $i=1,\ldots ,\dim \mathfrak {l}$ ,

(3.4) $$ \begin{align} { \lceil e^{c_{i}T_{D}} \rceil \leq e^{c_{i}T_{D}}e^{({D-J_L})/{\dim \mathfrak{l}}}. } \end{align} $$

For $r_0>0$ and $C_0 \geq 1$ from equation (3.1), fix $0<r_D=r_D(Q_\infty ^0)<\min (r_0,1/2)$ small enough so that

(3.5) $$ \begin{align} { B_{2^{{1}/{\min\mathbf{c}}}C_0 r_D^{{1}/{\max\mathbf{c}}}T_D}^{L,\mathbf{c}} \subset B_{\min(r_0,({1}/{2})r(Q_\infty))}^{L} \quad \text{and}\quad B_{r_D}^G (Y\smallsetminus Q_\infty) \subset Y\big(\tfrac{1}{2}r(Q_\infty)\big). } \end{align} $$

Lemma 3.4. For any $Q_{\infty }^0 \subset X$ and $D>J_L$ , we fix the above notation. Let $y\in Y\smallsetminus Q_\infty $ and $I=\{t\in \mathbb {N}\ |\ a_ty\in Q_\infty \}$ . For any non-negative integer T, let

$$ \begin{align*} E_{y,T} = \{ z\in B_{r_D}^{L}\cdot y \ |\ \text{ for all } t\in\{1,\ldots,T\}\smallsetminus I,\, d_Y(a_ty,a_tz)\leq r_D\}.\end{align*} $$

The set $E_{y,T}$ can be covered by $Ce^{D|I\cap \{1,\ldots ,T\}|} \kern1.5pt d_{\mathbf {c}}$ -balls of radius $r_D^{{1}/{\max \mathbf {c}}}e^{-T}$ , where C is a constant depending on $Q_\infty ^0$ and D, but independent of T.

Proof. For $s\in \{0,\ldots , T_{D}-1\}$ and $k\in \mathbb {Z}_{\geq 0}$ , let us denote $I_{s,k}(T_{D})= \{s,s+T_{D},\ldots , s+kT_{D}\}$ and

$$ \begin{align*} E_{y,k}^{s}= \{z\in B_{r_D}^{L}\cdot y : \text{ for all } t \in I_{s,k}(T_{D}) \smallsetminus I, d_{Y}(a_{t}y,a_{t}z)\leq r_D \}. \end{align*} $$

Following the proof of [Reference Lim, de Saxcé and ShapiraLSS19, Lemma 2.4] with $E_{y,k}^{s}$ instead of $E_{y,T}$ , we obtain the following claim.

Claim. The set $E_{y,k}^{s}$ can be covered by $C_{s}e^{(J_L(T_{D}-1)+D)|I\cap I_{s,k}(T_{D})|} d_{\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+kT_{D})}$ , where $C_{s}$ is a constant depending on $Q_\infty ^0$ , D, and s, but independent of k.

Proof of the claim

We prove the claim by induction on k. Since the number of $d_{\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}} e^{-s}$ needed to cover $B_{r_D}^L \cdot y$ is bounded by a constant $C_s$ depending on $Q_\infty ^0$ , D, and s, the claim holds for $k=0$ .

Suppose that $E_{y,k-1}^s$ can be covered by $N_{k-1}=C_s e^{(J_L(T_{D}-1)+D)|I\cap I_{s,k-1}(T_{D})|}$ $d_{\mathbf {c}}$ -balls $\{B_j:j=1,\ldots , N_{k-1}\}$ of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+(k-1)T_{D})}$ . By the inequality in equation (3.4), any $d_{\mathbf {c}}$ -ball of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+(k-1)T_D)}$ can be covered by

$$ \begin{align*} \prod_{i=1}^{\dim\mathfrak{l}} \bigg\lceil \frac{e^{-(s+(k-1)T_D) c_i }}{e^{-(s+kT_D) c_i }} \bigg\rceil &= \prod_{i=1}^{\dim\mathfrak{l}}\lceil e^{T_D c_i} \rceil \leq \prod_{i=1}^{\dim\mathfrak{l}} e^{c_{i}T_{D}}e^{({D-J_L})/{\dim \mathfrak{l}}}\\ &= e^{J_L T_D}e^{D-J_L}= e^{J_L(T_D-1)+D} \end{align*} $$

$d_{\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+kT_D)}$ . Thus, if $s+kT_D \in I$ , then $E_{y,k}^s$ can be covered by $N_k=e^{J_L(T_D-1)+D}N_{k-1} d_{\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+kT_D)}$ .

Suppose that $s+kT_D \notin I$ . Since $E_{y,k}^s \subset E_{y,k-1}^s$ , the sets $E_{y,k}^s \cap B_j$ with $j=1,\ldots , N_{k-1}$ cover $E_{y,k}^s$ . We now claim that for any $1\leq j \leq N_{k-1}$ and $x_1,x_2\in E_{y,k}^s \cap B_j$ , we have

$$ \begin{align*}d_{L,\mathbf{c}}(x_1,x_2)\leq 2^{{1}/{\min\mathbf{c}}}C_0 r_D^{{1}/{\max\mathbf{c}}}e^{-(s+kT_D)}.\end{align*} $$

Indeed, since $B_j$ is a $d_{L,\mathbf {c}}$ -ball of radius $C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+(k-1)T_{D})}$ and $x_1,x_2 \in B_j \subset B_{r_D}^L \cdot y$ , there are $h\in B_{r_D}^L$ and $h_1,h_2\in B_{C_0 r_D^{{1}/{\max \mathbf {c}}}e^{-(s+(k-1)T_{D})}}^{L,\mathbf {c}}$ such that $x_1=h_1 hy$ and $x_2=h_2 hy$ . It follows from $s+kT_d \notin I$ and $x_1,x_2\in E_{y,k}^s$ that $a^{s+kT_d}y \subset Y \smallsetminus Q_\infty $ and $d_Y(a^{s+kT_D} y, a^{s+kT_D} x_\ell )\leq r_D$ for $\ell =1,2$ , and hence by equation (3.5), we have $a^{s+kT_D} x_1 \in B_{r_D}^G (Y \smallsetminus Q_\infty ) \subset Y(\tfrac 12 r(Q_\infty ))$ and $d_Y(a^{s+kT_D} x_1, a^{s+kT_D} x_2)\leq 2r_D$ . Observe that by equation (3.5),

$$ \begin{align*} a^{s+kT_D}h_1 h_2^{-1} a^{-(s+kT_D)} &\subset a^{s+kT_D}B_{2^{{1}/{\min\mathbf{c}}}C_0 r_D^{{1}/{\max\mathbf{c}}}e^{-(s+(k-1)T_{D})}}^{L,\mathbf{c}}a^{-(s+kT_D)}\\ &= B_{2^{{1}/{\min\mathbf{c}}}C_0 r_D^{{1}/{\max\mathbf{c}}}e^{T_{D}}}^{L,\mathbf{c}} \subset B_{\min(r_0,({1}/{2})r(Q_\infty))}^L. \end{align*} $$

Thus, it follows from equation (3.1) and the above observations that

$$ \begin{align*} 2r_D \geq d_Y (a^{s+kT_D}x_1,a^{s+kT_D}x_2) &= d_L(a^{s+kT_D}h_1h_2^{-1}a^{-(s+kT_D)},\mathrm{id}) \\ &\geq \frac{1}{C_0} d_\infty (a^{s+kT_D}h_1h_2^{-1}a^{-(s+kT_D)},\mathrm{id})\\ &= \frac{1}{C_0} \max_{i=1,\ldots,\dim\mathfrak{l}} e^{c_i(s+kT_D)} |(\log h_1h_2^{-1})_i|, \end{align*} $$

where $(\log h_1h_2^{-1})_i$ is the ith coordinate of $\log h_1h_2^{-1}$ with respect to the standard basis $\{e_i: 1\leq i\leq \dim \mathfrak {l}\}$ of $\mathfrak {l}$ . Since $L=U$ or $L=W$ , that is, a commutative subgroup of G, for each $i=1,\ldots ,\dim \mathfrak {l}$ , we have

$$ \begin{align*} |(\log h_1h_2^{-1})_i|=|(\log h_1 - \log h_2)_i|\leq 2r_D C_0 e^{-c_i(s+kT_D)}. \end{align*} $$

Note that

$$ \begin{align*} d_{L,\mathbf{c}}(x_1,x_2)=d_{L,\mathbf{c}}(h_1,h_2) =\max_{i=1,\ldots,\dim\mathfrak{l}} |(\log h_1 - \log h_2)_i|^{{1}/{c_i}}. \end{align*} $$

Therefore, we have

$$ \begin{align*} d_{L,\mathbf{c}}(x_1,x_2) \leq \max_{i=1,\ldots,\dim\mathfrak{l}} (2r_D C_0)^{{1}/{c_i}}e^{-(s+kT_D)}\leq 2^{{1}/{\min\mathbf{c}}}C_0 r_D^{{1}/{\max\mathbf{c}}}e^{-(s+kT_D)}. \end{align*} $$

It follows from the claim that $E_{y,k}^s \cap B_j$ is contained in a single $d_{L,\mathbf {c}}$ -ball of radius $C_0 r_D^{{1}/{\max \mathbf {c}}} e^{-(s+kT_D)}$ for each $j=1,\ldots ,N_{k-1}$ . Hence, $E_{y,k}^s$ can be covered by $N_k=N_{k-1} d_{L,\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}} e^{-(s+kT_D)}$ .

Now, for any non-negative integer T, we can find $s\in \{0,\ldots ,T_{D}-1\}$ and $k\in \mathbb {Z}_{\geq 0}$ such that

$$ \begin{align*} { T_{D}|I\cap I_{s,k}(T_{D})| \leq |I\cap \{1,\ldots,T\}|\quad\text{and}\quad T-T_{D}<s+kT_{D}\leq T } \end{align*} $$

from the pigeon hole principle. By the above observation, $E_{y,T}\subset E_{y,k}^{s}$ can be covered by $C_{s}e^{(J_L(T_{D}-1)+D)|I\cap I_{s,k}(T_{D})|} d_{\mathbf {c}}$ -balls of radius $C_0 r_D^{{1}/{\max \mathbf {c}}} e^{-(s+kT_D)}$ . Since $T-T_{D}<s+kT_{D}\leq T$ and $D>J_L$ , $E_{y,T}$ can be covered by $(\max _{0\leq s\leq T_{D}-1}C_s) e^{D|I\cap \{1,\ldots ,T\}|} d_{\mathbf {c}}$ -balls of radius $C_0 e^{T_D} r_D^{{1}/{\max \mathbf {c}}} e^{-T}$ . Hence, there exists a constant $C>0$ depending on $Q_\infty ^0$ , r, and D, but independent of T such that $E_{y,T}$ can be covered by $C e^{D|I\cap \{1,\ldots ,T\}|} d_{\mathbf {c}}$ -balls of radius $r_D^{{1}/{\max \mathbf {c}}} e^{-T}$ .

4 Upper bound for Hausdorff dimension of $\mathbf {Bad}_{A}(\epsilon )$

In this section, we will prove Theorem 1.2 by constructing an a-invariant probability measure on Y with large entropy. Here and in the next section, we will consider the dynamical entropy of a instead of $a^{-1}$ in contrast to §2. Hence, let us use the following notation. For a given partition $\mathcal {Q}$ of Y and a integer $q\geq 1$ , we denote

$$ \begin{align*} \mathcal{Q}^{(q)}= \bigvee_{i=0}^{q-1} a^{-i}\mathcal{Q}. \end{align*} $$

4.1 Constructing measure with entropy lower bound

Let us denote by $\overline {X}$ and $\overline {Y}$ the one-point compactifications of X and Y, respectively. Let $\mathcal {A}$ be a given countably generated $\sigma $ -algebra of X or Y. We denote by $\overline {\mathcal {A}}$ the $\sigma $ -algebra generated by $\mathcal {A}$ and $\{\infty \}$ . The diagonal action $a_t$ is extended to the action on $\overline {X}$ and $\overline {Y}$ by $a_t(\infty )=\infty $ for $t\in \mathbb {R}$ . For a finite partition $\mathcal {Q}=\{Q_1,\ldots ,Q_N,Q_\infty \}$ of Y which has only one non-compact element $Q_\infty $ , denote by $\overline {\mathcal {Q}}$ the finite partition $\{Q_1, \ldots , Q_N, \overline {Q_\infty }\overset {\operatorname {def}}{=} Q_\infty \cup \{\infty \}\}$ of $\overline {Y}$ . Note that $\overline {\mathcal {Q}^{(q)}}=\overline {\mathcal {Q}}^{(q)}$ for any $q\in \mathbb {N}$ . Denote by $\mathscr {P}(X)$ the space of probability measures on X, and use similar notation for Y, $\overline {X}$ , and $\overline {Y}$ .

In this subsection, we construct an a-invariant measure on $\overline {Y}$ with a lower bound on the conditional entropy for the proof of Theorem 1.2. Here, the conditional entropy will be computed with respect to the $\sigma $ -algebras constructed in §2. If $x_A$ has no escape of mass, such measure was constructed in [Reference Lim, de Saxcé and ShapiraLSS19, Proposition 2.3]. The following proposition generalizes the measure construction for $x_A$ terms with some escape of mass.

Proposition 4.1. For $A\in M_{m,n}(\mathbb {R})$ fixed, let

$$ \begin{align*}\eta_A=\sup\{\eta:x_A \ \textrm{has} \ \eta\textrm{-escape of mass on average}\}.\end{align*} $$

Then, there exists $\mu _A\in \mathscr {P}(\overline {X})$ with $\mu _A(X)=1-\eta _A$ such that for any $\epsilon>0$ , there exists an a-invariant measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ satisfying:

  1. (1) $\operatorname {Supp}{\overline {\mu }}\subset \mathcal {L}_\epsilon \cup (\overline {Y}\smallsetminus Y)$ ;

  2. (2) $\pi _*\overline {\mu }=\mu _A$ , in particular, there exists an a-invariant measure $\mu \in \mathscr {P}(Y)$ such that

    $$ \begin{align*}\overline{\mu}=(1-\eta_A)\mu+\eta_A\delta_{\infty},\end{align*} $$
    where $\delta _\infty $ is the dirac delta measure on $\overline {Y}\smallsetminus Y$ ;
  3. (3) let $\mathcal {A}^W$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=W$ , and let $\mathcal {A}^W_\infty $ be as in equation (2.12). Then, we have

    $$ \begin{align*}h_{\overline{\mu}}(a|\overline{\mathcal{A}^W_\infty})\ge 1-\eta_A-r_{1}(m- \dim_H \mathbf{Bad}_A(\epsilon)).\end{align*} $$

Remark 4.2.

  1. (1) Note that if $\eta _A>0$ , then $x_{A}$ has $\eta _A$ -escape of mass on average;

  2. (2) one can check that $\eta _A=0$ if and only if $x_{A}$ is heavy, which is defined in [Reference Lim, de Saxcé and ShapiraLSS19, Definition 1.1].

Proof. Since $x_{A}$ has $\eta _A$ -escape of mass on average but no more than $\eta _A,$ we may fix an increasing sequence of integers $\{k_i\}_{i\geq 1}$ such that

$$ \begin{align*}\frac{1}{k_i}\sum_{k=0}^{k_i-1}\delta_{a^k x_A}\overset{\operatorname{w}^*}{\longrightarrow}\mu_A\in\mathscr{P}(\overline{X})\end{align*} $$

with $\mu _A(X)= 1-\eta _A$ .

Let us denote by $\mathbb {T}^{m}= [0,1]^{m}/\!\sim $ the torus in $\mathbb {R}^{m}$ , where the equivalence relation is modulo $1.$ Consider the increasing family of sets

$$ \begin{align*}R^{A,T}\overset{\operatorname{def}}{=}\{b\in \mathbb{T}^m|\text{ for all } t\ge T,a_t y_{A,b}\in\mathcal{L}_\epsilon\}\cap\mathbf{Bad}_A(\epsilon).\end{align*} $$

By Proposition 3.2 and Remark 3.3, $\bigcup _{T=1}^\infty R^{A,T}$ has Hausdorff dimension equal to $\dim _H \mathbf {Bad}_A(\epsilon )$ . For any $\gamma>0$ , it follows that there exists $T_\gamma \in \mathbb {N}$ satisfying $\dim _H R^{A,T_\gamma }\geq \dim _H \mathbf {Bad}_A(\epsilon )-\gamma $ .

Let $\phi _A:\mathbb {T}^{m}\to Y$ be the map defined by $\phi _A(b)=y_{A,b}$ . Note that $\phi _A$ is a one-to-one Lipschitz map between $\mathbb {T}^m$ and $\phi _A(\mathbb {T}^m)$ , so we may consider a quasinorm on $\phi _A(\mathbb {T}^m)$ induced from the $\mathbf {r}$ -quasinorm on $\mathbb {R}^m$ and denote it again by $\|\cdot \|_{\mathbf {r}}$ .

For each $k_i\ge T_\gamma $ , let $S_i$ be a maximal $e^{-k_i}$ -separated subset of $R^{A,T_\gamma }$ with respect to the $\mathbf {r}$ -quasinorm. By Lemma 3.1(3),

$$ \begin{align*} {\liminf_{i\to\infty}\frac{\log|S_i|}{k_i}\ge \underline{\dim}_{\mathbf{r}}(R^{A,T_\gamma})\ge 1-r_{1}(m+\gamma- \dim_H \mathbf{Bad}_A(\epsilon)).} \end{align*} $$

Let $\nu _i\overset {\operatorname {def}}{=}({1}/{|S_i|})\sum _{b\in S_i}\delta _{y_{A,b}}$ be the normalized counting measure on the set $D_i\overset {\operatorname {def}}{=}\{y_{A,b}: b\in S_i\}\subset Y$ . Extracting a subsequence if necessary, we may assume that

$$ \begin{align*}\mu_i\overset{\operatorname{def}}{=}\frac{1}{k_i}\sum_{k=0}^{k_i-1}a_*^k\nu_i\overset{\operatorname{w}^*}{\longrightarrow}\mu^\gamma\in\mathscr{P}(\overline{Y}).\end{align*} $$

The measure $\mu ^{\gamma }$ is a-invariant since $a_* \mu _i -\mu _i$ goes to zero measure.

Choose any sequence of positive real numbers $(\gamma _j)_{j\geq 1}$ converging to zero and let $\{\mu ^{\gamma _j}\}$ be a family of a-invariant probability measures on $\overline {Y}$ obtained from the above construction for each $\gamma _j$ . Extracting a subsequence again if necessary, we may take a $\text {weak}^*$ -limit measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ of $\{\mu ^{\gamma _j}\}$ . We prove that $\overline {\mu }$ is the desired measure. The measure $\overline {\mu }$ is clearly a-invariant.

(1) We show that for all $\gamma>0$ , $\mu ^\gamma (Y\setminus \mathcal {L}_\epsilon )=0$ . For any $b\in S_i\subseteq R^{A,T_\gamma }$ , $a_T y_{A,b}\in \mathcal {L}_\epsilon $ holds for $T>T_\gamma $ . Thus, we have

$$ \begin{align*} \mu_i(Y\setminus\mathcal{L}_\epsilon)&=\frac{1}{k_i}\sum_{k=0}^{k_i-1}a^k_{*}\nu_i(Y\setminus\mathcal{L}_\epsilon)= \frac{1}{k_i}\sum_{k=0}^{T_\gamma}a^k_{*}\nu_i(Y\setminus\mathcal{L}_\epsilon)\\ &= \frac{1}{k_{i} |S_{i}|}\sum_{y\in D_{i}, 0\leq k \leq T_{\gamma}} \delta_{a^{k}y}(Y\setminus\mathcal{L}_\epsilon) \leq \frac{T_\gamma}{k_i}. \end{align*} $$

By taking $k_i\to \infty $ , we have $\mu ^{\gamma }(Y\setminus \mathcal {L}_\epsilon )=0$ for arbitrary $\gamma>0$ , and hence

$$ \begin{align*}\overline{\mu}(Y\setminus\mathcal{L}_\epsilon)=\lim_{j\to\infty}\mu^{\gamma_j}(Y\setminus\mathcal{L}_\epsilon)=0.\end{align*} $$

(2) For all $\gamma>0$ , $\pi _*\mu ^\gamma =\mu _A$ since $\pi _*\nu _i=\delta _{x_{A}}$ for all $i\ge 1$ . It follows that $\pi _*\overline {\mu }=\mu _A$ . Hence,

$$ \begin{align*}\overline{\mu}(\overline{Y}\setminus Y)=\lim_{j\to\infty}\mu^{\gamma_j}(\overline{Y}\setminus Y)=\mu_A(\overline{X}\setminus X)=\eta_A,\end{align*} $$

so we have a decomposition $\overline {\mu }=(1-\eta _A)\mu +\eta _A\delta _\infty $ for some a-invariant $\mu \in \mathscr {P}(Y)$ .

(3) We first fix any $D>J_W =1$ and $Q^0_\infty \subset X$ such that $X\smallsetminus Q_\infty ^0$ has compact closure. As in [Reference Lim, de Saxcé and ShapiraLSS19, Proof of Theorem 4.2, Claim 2], we can construct a finite partition $\mathcal {Q}$ of Y satisfying:

  • $\mathcal {Q}$ contains an atom $Q_\infty $ of the form $\pi ^{-1}(Q_\infty ^0)$ ;

  • $\text { for all } Q\in \mathcal {Q}\smallsetminus \{Q_\infty \}$ , $\operatorname {\mathrm {diam}} Q<r_D=r_D(Q_\infty ^0)$ , where $r_D$ is from equation (3.5);

  • $\text { for all } Q\in \mathcal {Q},\text { for all } j\geq 1,\; \mu ^{\gamma _j}(\partial Q)=0$ .

Remark that for all $i\geq 1$ , $D_i \subset \phi _A(\mathbb {T}^m)$ , which is a compact set in Y; therefore, we can choose $Q^0_{\infty }$ so that

(4.1) $$ \begin{align} { Q_\infty \cap D_i = \varnothing. } \end{align} $$

We claim that it suffices to show the following statement. For all $q\ge 1$ ,

(4.2) $$ \begin{align} {\frac{1}{q}H_{\overline{\mu}}(\overline{\mathcal{Q}}^{(q)}|\overline{\mathcal{A}^W_\infty})\ge 1-r_{1}(m-\dim_H \mathbf{Bad}_A(\epsilon))-D\overline{\mu}(\overline{Q_\infty}).} \end{align} $$

Indeed, by taking $q\to \infty $ , we have

$$ \begin{align*}h_{\overline{\mu}}(a|\overline{\mathcal{A}^W})\ge 1-r_{1}(m- \dim_H \mathbf{Bad}_A(\epsilon))-D\overline{\mu}(\overline{Q_\infty}).\end{align*} $$

Taking $D\to 1$ and $Q_\infty ^0\subset X$ such that $\overline {\mu }(\overline {Q_\infty }) \to \overline {\mu }(\overline {Y}\setminus Y)=\eta _A$ and $D\to 1$ , we conclude equation (3).

In the rest of the proof, we show the inequality in equation (4.2). It is clear if $\overline {\mu }(Q_\infty )=1$ , so assume that $\overline {\mu }(Q_\infty )<1$ , and hence for all large enough $j\geq 1$ , $\mu ^{\gamma _j}(Q_\infty )<1$ . Now, we fix such $j\geq 1$ and write temporarily $\gamma =\gamma _j$ .

Choose $\beta>0$ such that $\mu ^\gamma (Q_\infty )<\beta <1$ . For large enough $i\geq 1$ , we have

$$ \begin{align*} { \mu_i(Q_\infty)=\frac{1}{k_i|S_i|}\sum_{y\in D_i, 0\le k<k_i}\delta_{a^k y}(Q_\infty) =\frac{1}{k_i}\sum_{0\le k<k_i}\delta_{a^k x_{A}}(Q^0_\infty) < \beta. } \end{align*} $$

In other words, there exist at most $\beta k_i$ number of $a^k x_A$ terms in $Q^0_\infty $ , and thus for any $y\in D_{i}$ , we have

$$ \begin{align*} |\{k\in\{0,\ldots,k_{i}-1\}: a^{k}y \in Q_{\infty}\}| < \beta k_{i}. \end{align*} $$

From Lemma 3.4 with $L=W$ and equation (4.1), if Q is any non-empty atom of $\mathcal {Q}^{(k_i)}$ , fixing any $y\in D_i\cap Q$ , the set

$$ \begin{align*} { D_{i}\cap Q = D_{i}\cap [y]_{\mathcal{Q}^{(k_i)}} \subset E_{y,k_{i}-1} } \end{align*} $$

can be covered by $Ce^{D\beta k_{i}}$ many $r_D^{1/r_1}e^{-k_{i}}$ -balls for $d_{\mathbf {r}}$ , where C is a constant depending on $Q_\infty ^0$ and D, but not on $k_i$ . Since $D_{i}$ is $e^{-k_{i}}$ -separated with respect to $d_{\mathbf {r}}$ and $r_D^{1/r_{1}}<\tfrac 12$ , we get

(4.3) $$ \begin{align} {\mathrm{Card}(D_i \cap Q)\leq Ce^{D\beta k_i }.} \end{align} $$

Now let $\mathcal {A}^W=(\mathcal {P}^W)_0^\infty =\bigvee _{i=0}^{\infty }a^i \mathcal {P}^W$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=W$ , and let $\mathcal {A}^W_\infty $ be as in equation (2.12).

Claim. $H_{\nu _i}(\mathcal {Q}^{(k_i)}|\mathcal {A}^W_\infty ) = H_{\nu _i}(\mathcal {Q}^{(k_i)})$ .

Proof of the claim

Using the continuity of entropy, we have

$$ \begin{align*} {H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^W_\infty)=\lim_{\ell\to\infty}H_{\nu_i}(\mathcal{Q}^{(k_i)}|(\mathcal{P}^W)_\ell^\infty).} \end{align*} $$

Now we show $H_{\nu _i}(\mathcal {Q}^{(k_i)}|(\mathcal {P}^W)_\ell ^\infty ) = H_{\nu _i}(\mathcal {Q}^{(k_i)})$ for all large enough $\ell \geq 1$ . Let $E_\delta $ be the dynamical $\delta $ -boundary of $\mathcal {P}$ as in Lemma 2.7 for $\mu $ and $r_0$ . As mentioned in Remark 2.4, we may assume that there exists $y\in \phi _A(\mathbb {T}^m)$ such that $y\notin \partial \mathcal {P}$ . Since $E_\delta = \bigcup _{k=0}^\infty a^k \partial _{d_0e^{-k\alpha }\delta }\mathcal {P}$ , there exists $\delta>0$ such that $y\in Y \setminus E_{\delta }$ . For any $\ell \geq 1$ , we have $a^{-\ell }y \in Y\setminus a^{-\ell }E_\delta \subset Y\setminus E_\delta $ . Hence, it follows from equation (2.6) and Proposition 2.8 that

$$ \begin{align*} [y]_{(\mathcal{P}^W)_\ell^\infty} = a^\ell[a^{-\ell}y]_{(\mathcal{P}^W)_0^\infty} = a^\ell[a^{-\ell}y]_{\mathcal{A}^W} \supset a^{\ell} B_{\delta}^{W} a^{-\ell} y \supset B_{d_0 e^{\alpha\ell}\delta}^{W} y. \end{align*} $$

Since the support of $\nu _i$ is a set of finite points on a single compact W-orbit $\phi _A(\mathbb {T}^m)$ , $\nu _i$ is supported on a single atom of $(\mathcal {P}^W)_\ell ^\infty $ for all large enough $\ell \geq 1$ . This proves the claim.

Combining equation (4.3) and the above claim, it follows that

(4.4) $$ \begin{align} { H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^W_\infty)= H_{\nu_i}(\mathcal{Q}^{(k_i)}) \geq \log |S_i|-D\beta k_i-\log C. } \end{align} $$

For any $q\ge 1$ , write the Euclidean division of large enough $k_i-1$ by q as

$$ \begin{align*}k_i-1=qk'+s \ \textrm{with} \ s\in\{0,\ldots,q-1\}.\end{align*} $$

By subadditivity of the entropy with respect to the partition, for each $p\in \{0,\ldots ,q-1\}$ ,

$$ \begin{align*}H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^W_\infty)\leq H_{a^{p}\nu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^W_\infty)+\cdots+H_{a^{p+qk'}\nu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^W_\infty)+2q\log |\mathcal{Q}|.\end{align*} $$

Summing those inequalities for $p=0,\ldots ,q-1$ , and using the concave property of entropy with respect to the measure, we obtain

(4.5) $$ \begin{align} qH_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^W_\infty)&\leq\sum_{k=0}^{k_i-1}H_{a^k \nu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^W_\infty)_0^M+2q^2\log |\mathcal{Q}|\nonumber\\&\leq k_iH_{\mu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^W_\infty)+2q^2\log |\mathcal{Q}|, \end{align} $$

and it follows from equation (4.4) that

$$ \begin{align*} \frac{1}{q}H_{\mu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^W_\infty)&\ge \frac{1}{k_i}H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^W_\infty)-\frac{2q\log |\mathcal{Q}|}{k_i}\\ &\ge \frac{1}{k_i}(\log |S_i|-D\beta k_i -\log C - 2q\log|\mathcal{Q}|). \end{align*} $$

Now we can take $i\to \infty $ because the atoms Q of $\overline {\mathcal {Q}}$ and hence of $\overline {\mathcal {Q}}^{(q)}$ satisfy $\mu ^\gamma (\partial Q)=0$ . Also, the constants C and $|\mathcal {Q}|$ are independent to $k_i$ . Thus, we obtain

$$ \begin{align*} \frac{1}{q}H_{\mu^\gamma}(\overline{\mathcal{Q}}^{(q)}|\overline{\mathcal{A}^W_\infty}) \ge 1-r_{1}(m+\gamma- \dim_H \mathbf{Bad}_A(\epsilon))-D\beta. \end{align*} $$

By taking $\beta \to \overline {\mu }(\overline {Q_\infty })$ and $\gamma =\gamma _j \to 0$ , the inequality in equation (4.2) follows.

4.2 The proof of Theorem 1.2

In this subsection, we will estimate the dimension upper bound in Theorem 1.2 using the a-invariant measure with large relative entropy constructed in Proposition 4.1 and the effective variational principle in Theorem 2.12. To use the effective variational principle, we need the following lemma.

For $x\in X$ and $H\geq 1$ , we set

$$ \begin{align*}\textrm{ht}(x)\overset{\operatorname{def}}{=}\sup\{\|gv\|^{-1}: x=gSL_d(\mathbb{Z}), v\in\mathbb{Z}^d\setminus\{0\}\},\end{align*} $$
$$ \begin{align*}X_{\leq H}\overset{\operatorname{def}}{=}\{x\in X: \textrm{ht}(x)\leq H \},\quad Y_{\leq H}\overset{\operatorname{def}}{=}\pi^{-1}(X_{\le H}).\end{align*} $$

Note that $\textrm {ht}(x) \geq 1$ for any $x\in X$ by Minkowski’s theorem, and $X_{\leq H}$ and $Y_{\leq H}$ are compact sets for all $H\geq 1$ by Mahler’s compact criterion.

Lemma 4.3. Let $\mathcal {A}$ be a countably generated sub- $\sigma $ -algebra of Borel $\sigma $ -algebra which is $a^{-1}$ -descending and W-subordinate. Let us fix $y\in Y_{\leq H}$ and suppose that $B^{W,\mathbf {r}}_{\delta }\cdot y\subset [y]_{\mathcal {A}}\subset B^{W,\mathbf {r}}_{r}\cdot y$ for some $0<\delta <r$ . For any $0<\epsilon <1$ , if $j_1\ge \log ((2dH^{d-1})^{{1}/{r_m}}\delta ^{-1})$ and $j_2\ge \log ((dH^{d-1})^{{1}/{s_n}}\epsilon ^{-{n}/{d}})$ , then $\tau _y^{a^{j_1}\mathcal {A}}(a^{-j_2}\mathcal {L}_\epsilon )\leq 1-e^{-j_1-j_2}r^{-1}\epsilon ^{{m}/{d}}$ , where $\tau _{y}^{a^{j_1}\mathcal {A}}$ is as in §2.4.

Proof. For $x=\pi (y)\in X_{\leq H}$ , there exists $g\in SL_d(\mathbb {R})$ such that $x=gSL_d(\mathbb {Z})$ and $\inf _{v\in \mathbb {Z}^d\setminus \{0\}}\|gv\|\ge H^{-1}$ . By Minkowski’s second theorem with a convex body $[-1,1]^d$ , we can choose vectors $gv_1,\ldots ,gv_d$ in $g\mathbb {Z}^d$ so that $\prod _{i=1}^{d}\|gv_i\|\leq 1$ . Then, for any $1\leq i\leq d$ ,

$$ \begin{align*}\|gv_i\|\leq \prod_{j\neq i}\|gv_j\|^{-1} \leq H^{d-1}.\end{align*} $$

Let $\Delta \subset \mathbb {R}^d$ be the parallelepiped generated by $gv_1,\ldots , gv_d$ , then $\|b\|\leq dH^{d-1}$ for any $b\in \Delta $ . It follows that $\|b^+\|_{\mathbf {r}}\leq (dH^{d-1})^{{1}/{r_m}}$ and $\|b^-\|_{\mathbf {s}}\leq (dH^{d-1})^{{1}/{s_n}}$ for any $b=(b^+,b^-)\in \Delta $ , where $b^+\in \mathbb {R}^m$ and $b^-\in \mathbb {R}^n$ . Note that the set $\pi ^{-1}(x)\subset Y$ is parameterized as follows:

$$ \begin{align*}\pi^{-1}(x)=\{w(b)g\Gamma\in Y: b\in\Delta\}.\end{align*} $$

Write $y=w(b_0)g\Gamma $ for some $b_0=(b_0^+,b_0^-)\in \Delta $ . Denote by $V_y\subset W$ the shape of $\mathcal {A}$ -atom so that $V_y\cdot y=[y]_{a^{j_1}\mathcal {A}}$ , and $\Xi \subset \mathbb {R}^m$ the corresponding set to $V_y$ containing $0$ given by the canonical bijection between W and $\mathbb {R}^m$ . Since $a^{j_1}$ expands the $\mathbf {r}$ -quasinorm with the ratio $e^{j_1}$ , we have $B^{W,\mathbf {r}}_{e^{j_1}\delta }\cdot y\subset [y]_{a^{j_1}\mathcal {A}}\subset B^{W,\mathbf {r}}_{e^{j_1}r}\cdot y$ , that is, $B^{\mathbb {R}^m,\mathbf {r}}_{e^{j_1}\delta }\subset \Xi \subset B^{\mathbb {R}^m,\mathbf {r}}_{e^{j_1}r}.$ Then the atom $[y]_{a^{j_1}\mathcal {A}}$ is parameterized as follows:

$$ \begin{align*}[y]_{a^{j_1}\mathcal{A}}=\{w(b)g \Gamma: b=(b^+,b^-_0), b^+\in b^+_0+\Xi\},\end{align*} $$

and $\tau _y^{a^{j_1}\mathcal {A}}$ can be considered as the normalized Lebesgue measure on the set $b^+_0+\Xi \subset \mathbb {R}^m$ .

Let us consider the following sets:

$$ \begin{align*}\Theta^+\overset{\operatorname{def}}{=}\{b^+\in\mathbb{R}^m: \|b^+\|_{\mathbf{r}}\leq e^{-j_2}\epsilon^{{m}/{d}}\} \quad \text{and}\quad \Theta^-\overset{\operatorname{def}}{=}\{b^-\in\mathbb{R}^n: \|b^-\|_{\mathbf{s}}\leq e^{j_2}\epsilon^{{n}/{d}}\}.\end{align*} $$

If $b=(b^+,b^-)\in \Theta ^+\times \Theta ^-$ , then $\|e^{\mathbf {r}j_2}b^+\|_{\mathbf {r}}\leq \epsilon ^{{m}/{d}}$ and $\|e^{-\mathbf {s}j_2}b^-\|_{\mathbf {s}}\leq \epsilon ^{{n}/{d}}$ , where $e^{\mathbf {r}j_2}b^+$ and $e^{-\mathbf {s}j_2}b^-$ denote the vectors such that $a^{j_2}b=(e^{\mathbf {r}j_2}b^+,e^{-\mathbf {s}j_2}b^-)$ . It follows that $w(b)g\Gamma \notin a^{-j_2}\mathcal {L}_\epsilon $ since

$$ \begin{align*}a^{j_2}w(b^+,b^-)g\Gamma=w(e^{\mathbf{r}j_2}b^+,e^{-\mathbf{s}j_2}b^-)a^{j_2}g\Gamma\notin\mathcal{L}_\epsilon\end{align*} $$

by the definition of $\mathcal {L}_\epsilon $ .

Now we claim that the set $\Theta ^+\times \{b_0^{-}\}$ is contained in the intersection of $(b_0^++\Xi )\times \{b_0^{-}\}$ and $\Theta ^+\times \Theta ^-$ . See Figure 1. It is enough to show that $\Theta ^+ \subset b_0^+ + \Xi $ and $b_0^- \in \Theta ^-$ . Since $\|b_0^-\|_s \leq (dH^{d-1})^{{1}/{s_n}}$ , the latter assertion follows from the assumption $j_2\ge \log ((dH^{d-1})^{{1}/{s_n}}\epsilon ^{-{n}/{d}})$ . To show the former assertion, fix any $b^+ \in \Theta ^+$ . By the quasi-metric property of $\|\cdot \|_{\mathbf {r}}$ as in equation (3.2), it follows from the assumptions $j_1\ge \log ((2dH^{d-1})^{{1}/{r_m}}\delta ^{-1})$ and $j_2\ge \log ((dH^{d-1})^{{1}/{s_n}}\epsilon ^{-{n}/{d}})$ that

$$ \begin{align*} \|b^+ - b_0^+\|_{\mathbf{r}} &\leq 2^{({1-r_m})/{r_m}}(\|b^+\|_{\mathbf{r}}+\|b_0^+\|_{\mathbf{r}})\leq 2^{({1-r_m})/{r_m}}(e^{-j_2}\epsilon^{{m}/{d}} + (dH^{d-1})^{{1}/{r_m}})\\ &\leq 2^{({1-r_m})/{r_m}}((dH^{d-1})^{-{1}/{s_n}}\epsilon + (dH^{d-1})^{{1}/{r_m}}) \leq 2^{({1-r_m})/{r_m}+1}(dH^{d-1})^{{1}/{r_m}} \\ &\leq e^{j_1}\delta. \end{align*} $$

Thus, we have $b^+ \in b_0^+ +B_{e^{j_1}\delta }^{\mathbb {R}^m,\mathbf {r}} \subset b_0^+ +\Xi $ , which concludes the former assertion.

Figure 1 Intersection of $\Theta ^{+}\times \Theta ^{-}$ and $[y]_{a^{j_{1}}\mathcal {A}}$ .

By the above claim, we obtain

$$ \begin{align*} 1-\tau_y^{a^{j_1}\mathcal{A}}(a^{-j_2}\mathcal{L}_\epsilon)&=\tau_y^{a^{j_1}\mathcal{A}}(Y\setminus a^{-j_2}\mathcal{L}_\epsilon)\\ &\geq \frac{m_{\mathbb{R}^m}(\Theta^{+})}{m_{\mathbb{R}^m}(b_0^{+}+\Xi)} \geq \frac{m_{\mathbb{R}^m}(B^{\mathbb{R}^m,\mathbf{r}}_{e^{-j_2}\epsilon^{{m}/{d}}})}{m_{\mathbb{R}^m}(B^{\mathbb{R}^m,\mathbf{r}}_{e^{j_1}r})} =\frac{e^{-j_2}\epsilon^{{m}/{d}}}{e^{j_1}r}. \end{align*} $$

This proves the lemma.

Proof of Theorem 1.2

Suppose that $A\in M_{m,n}(\mathbb {R})$ is not singular on average, and let

$$ \begin{align*}\eta_A=\sup\{\eta:x_A \ \textrm{has} \ \eta\textrm{-escape of mass}\}<1.\end{align*} $$

By Proposition 4.1, there is an a-invariant measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ such that

$$ \begin{align*}\operatorname{Supp}\overline{\mu}\subset\mathcal{L}_\epsilon\cup(\overline{Y}\setminus Y),\; \pi_*\overline{\mu}=\mu_A\in\mathscr{P}(\overline{X})\; \text{and}\; \overline{\mu}(\overline{Y}\setminus Y)=\mu_A(\overline{X}\setminus X)=\eta_A.\end{align*} $$

This measure can be represented by the linear combination

$$ \begin{align*}\overline{\mu}=(1-\eta_A)\mu+\eta_A\delta_\infty,\end{align*} $$

where $\delta _\infty $ is the dirac delta measure on $\overline {Y}\setminus Y$ and $\mu \in \mathscr {P}(Y)$ is a-invariant. There is a compact set $K\subset X$ such that $\mu _A(K)>0.99\mu _A(X)$ . We can choose $0<r<1$ such that $Y(r)\supset \pi ^{-1}(K)$ and $\mu (Y(r))>0.99$ . Note that the choice of r is independent of $\epsilon $ since $\mu _A$ is only determined by fixed A.

Let $\mathcal {A}^W$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=W$ , and let $\mathcal {A}^W_\infty $ be as in equation (2.12). It follows from equation (3) of Proposition 4.1 that

$$ \begin{align*}h_{\overline{\mu}}(a|\overline{\mathcal{A}^W_\infty})\ge (1-\eta_A)-r_1(m-\dim_H \mathbf{Bad}_A(\epsilon)).\end{align*} $$

Since the entropy function is linear with respect to the measure, it follows that

$$ \begin{align*} {h_{\mu}(a|\mathcal{A}^W_\infty)= \frac{1}{1-\eta_A}h_{\overline{\mu}}(a|\overline{\mathcal{A}^W_\infty})\ge 1-\frac{r_1}{1-\eta_A}(m-\dim_H \mathbf{Bad}_A(\epsilon)).} \end{align*} $$

By Proposition 2.10, we obtain

(4.6) $$ \begin{align} {H_{\mu}(\mathcal{A}^W|a\mathcal{A}^W)\ge 1-\frac{r_1}{1-\eta_A}(m-\dim_H \mathbf{Bad}_A(\epsilon)).} \end{align} $$

By Lemma 2.7, there exists $0<\delta <\min (({cr_0}/{16d_0})^2,r)$ such that the dynamical $\delta $ -boundary has measure $\mu (E_\delta )<0.01$ . Note that since $r_0$ depends only on G, the constants $C_1,C_2>0$ in Lemma 2.7 depend only on a and G, and hence $\delta $ is independent of $\epsilon $ even if the set $E_\delta $ might depend on $\epsilon $ . We write $Z=Y(r)\setminus E_\delta $ for simplicity. Note that $\mu (Z)\ge \mu (Y(r))-\mu (E_\delta )>0.98$ .

To apply Lemma 4.3, choose $H\geq 1$ such that

(4.7) $$ \begin{align} {Y(r) \subset Y_{\leq H}.} \end{align} $$

Note that the constant H depends only on r. Set

$$ \begin{align*}j_1=\lceil\log((2dH^{d-1})^{{1}/{r_m}}{\delta'}^{-1})\rceil\quad\text{and}\quad j_2=\lceil\log((dH^{d-1})^{{1}/{s_n}}\epsilon^{-{n}/{d}})\rceil,\end{align*} $$

where $\delta '>0$ will be determined below.

Let $\mathcal {A}{\kern-1pt}={\kern-1pt}a^{-k}\mathcal {A}^W$ for $k{\kern-1pt}={\kern-1pt}\lceil \log (2^{{1}/{r_m}}\epsilon ^{-{m}/{d}})\rceil {\kern-1pt}+{\kern-1pt}j_2$ . By Proposition 2.8, $[y]_{\mathcal {A}^W} {\kern-1pt}\subset{\kern-1pt} B_{r_0}^{W}\cdot y$ for all $y\in Y$ , and $B_{\delta }^{W}\cdot y \subset [y]_{\mathcal {A}^W}$ for all $y\in Z$ since $\delta < r$ . It follows from equation (3.1) that

$$ \begin{align*} \text{ for all } y\in Y,\ [y]_{\mathcal{A}^W}\subset B^{W,d_\infty}_{C_0 r_0}\cdot y \quad\text{and}\quad \text{ for all } y\in Z,\ B^{W,d_\infty}_{\delta/C_0}\cdot y\subset[y]_{\mathcal{A}^W}, \end{align*} $$

where $B^{W,d_\infty }_r$ is the $d_\infty $ -ball of radius r around the identity in W. For simplicity, we may assume that $r_0 < {1}/{C_0}$ by choosing $r_0$ small enough. This implies that

$$ \begin{align*} { \text{ for all } y\in Y,\ [y]_{\mathcal{A}^W}\subset B^{W,\mathbf{r}}_{1}\cdot y \quad\text{and}\quad \text{ for all } y\in Z,\ B^{W,\mathbf{r}}_{(\delta/C_0)^{{1}/{r_m}}}\cdot y\subset[y]_{\mathcal{A}^W}. } \end{align*} $$

Thus, for any $y\in Y$ ,

(4.8) $$ \begin{align} { [y]_{\mathcal{A}}=a^{-k}[a^k y]_{\mathcal{A}^W} \subset a^{-k}B^{W,\mathbf{r}}_{1} a^k \cdot y =B^{W,\mathbf{r}}_{e^{-k}}\cdot y \subset B^{W,\mathbf{r}}_{r'}\cdot y, } \end{align} $$

where $r'=2^{-{1}/{r_m}}e^{-j_2}\epsilon ^{{m}/{d}}$ . Similarly, it follows that for any $y\in a^{-k}Z$ ,

(4.9) $$ \begin{align} {B^{W,\mathbf{r}}_{\delta'}\cdot y\subset [y]_{\mathcal{A}}\subset B^{W,\mathbf{r}}_{r'}\cdot y,} \end{align} $$

where $\delta '=e^{-1}(\delta /C_0)^{{1}/{r_m}}r'$ .

Now we will use Corollary 2.13 with $L=W$ , $K=Y$ , and $B=B^{W,\mathbf {r}}_{r'}$ . Note that the maximal entropy contribution of W for $a^{j_1}$ is $j_1$ , and $\mu $ is supported on $a^{-j_2}\mathcal {L}_\epsilon $ since $\operatorname {Supp}\mu \subseteq \mathcal {L}_\epsilon $ and $\mu $ is a-invariant. Thus, we have

(4.10) $$ \begin{align} { B^{W,\mathbf{r}}_{r'}\operatorname{Supp} \mu \subset B^{W,\mathbf{r}}_{r'}a^{-j_2}\mathcal{L}_\epsilon =a^{-j_2}B^{W,\mathbf{r}}_{e^{j_2}r'}\mathcal{L}_\epsilon = a^{-j_2}B^{W,\mathbf{r}}_{2^{-{1}/{r_m}}\epsilon^{{m}/{d}}}\mathcal{L}_\epsilon \subset a^{-j_2}\mathcal{L}_{2^{-{d}/{mr_m}}\epsilon}} \end{align} $$

by using the triangular inequality of $\mathbf {r}$ -quasinorm as in equation (3.2) and the definition of $\mathcal {L}_\epsilon $ for the last inclusion. Using equation (4.8), it follows from equation (4.10) and Corollary 2.13 with $L=W$ , $K=Y$ , and $B=B^{W,\mathbf {r}}_{r'}$ that

(4.11) $$ \begin{align} H_\mu(\mathcal{A}|a^{j_1}\mathcal{A})\leq j_1+\int_Y\log\tau^{a^{j_1}\mathcal{A}}_y(a^{-j_2}\mathcal{L}_{2^{-{d}/{mr_m}}\epsilon})\,d\mu(y). \end{align} $$

Using equation (4.9), it follows from Lemma 4.3 with $\delta =\delta '$ and $r=r'$ that for any $y\in a^{-k} Z\cap Y_{\leq H}$ ,

$$ \begin{align*}\tau^{a^{j_1}\mathcal{A}}_y(a^{-j_2}\mathcal{L}_{2^{-{d}/{mr_m}}\epsilon})\leq 1-2^{-{1}/{r_m}}e^{-j_1-j_2}r'^{-1}\epsilon^{{m}/{d}}=1-e^{-j_1}, \end{align*} $$

and hence $-\log \tau ^{a^{j_1}\mathcal {A}}_y(a^{-j_2}\mathcal {L}_{2^{-{d}/{mr_m}}\epsilon })\ge e^{-j_1}$ . Since $\mu (a^{-k} Z\cap Y_{\leq H})\geq \tfrac 12$ , it follows from equation (4.11) that

(4.12) $$ \begin{align} 1-H_\mu(\mathcal{A}^W|a\mathcal{A}^W)&=1-\frac{1}{j_1}H_\mu(\mathcal{A}^W|a^{j_1}\mathcal{A}^W)=1-\frac{1}{j_1}H_\mu(\mathcal{A}|a^{j_1}\mathcal{A})\nonumber\\ &\ge-\frac{1}{j_1}\int_{a^{-k} Z\cap Y_{\leq H}}\log\tau^{a^{j_1}\mathcal{A}}_y(a^{-j_2}\mathcal{L}_{2^{-{d}/{mr_m}}\epsilon})\,d\mu(y) \ge \frac{e^{-j_1}}{2j_1}. \end{align} $$

Recall that $j_1$ is chosen by

$$ \begin{align*} j_1&=\lceil\log((2dH^{d-1})^{{1}/{r_m}} e(\delta/C_0)^{-{1}/{r_m}}2^{{1}/{r_m}}e^{j_2}\epsilon^{-{m}/{d}})\rceil\\ &\leq\lceil\log((2dH^{d-1})^{{1}/{r_m}+{1}/{s_n}} e^2 (\delta/C_0)^{-{1}/{r_m}}2^{{1}/{r_m}} \epsilon^{-{n}/{d}}\epsilon^{-{m}/{d}})\rceil\\ &\leq \log((2dH^{d-1})^{{1}/{r_m}+{1}/{s_n}} e^3 (\delta/C_0)^{-{1}/{r_m}}2^{{1}/{r_m}}) -\log\epsilon. \end{align*} $$

Here, the constants H and $\delta $ depend on fixed $A\in M_{m,n}(\mathbb {R})$ , not on $\epsilon $ . Combining equations (4.6) and (4.12), we obtain

$$ \begin{align*}m-\dim_H \mathbf{Bad}_A(\epsilon)\geq c(A)\frac{\epsilon}{\log(1/\epsilon)},\end{align*} $$

where the constant $c(A)>0$ depends only on d, $\mathbf {r}$ , $\mathbf {s}$ , and $A\in M_{m,n}(\mathbb {R})$ . It completes the proof.

5 Upper bound for Hausdorff dimension of $\mathbf {Bad}^{b}(\epsilon )$

In this section, as explained in the introduction, the target vector b is fixed and we only consider the unweighted setting, that is,

$$ \begin{align*} { \mathbf{r}=(1/m,\ldots,1/m)\quad\text{and}\quad \mathbf{s}=(1/n,\ldots,1/n). } \end{align*} $$

5.1 Constructing measure with entropy lower bound

Similar to §4.1, we will construct an a-invariant measure on Y with a lower bound on the conditional entropy to the $\sigma $ -algebra $\mathcal {A}^U_\infty $ obtained in equation (2.12) and Proposition 2.8 with $L=U$ . To control the amount of escape of mass for the desired measure, we need a modification of [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17, Theorem 1.1] as Proposition 5.3 below.

For any compact set $\mathfrak {S}\subset X$ and positive integer $k>0$ , and any $0<\eta <1$ , let

$$ \begin{align*} F_{\eta,\mathfrak{S}}^k\overset{\operatorname{def}}{=}\bigg\{A\in \mathbb{T}^{mn}\subset M_{m,n}(\mathbb{R}):\frac{1}{k}\sum_{i=0}^{k-1}\delta_{a^i x_A} (X\setminus \mathfrak{S})<\eta\bigg\}. \end{align*} $$

Given a compact set $\mathfrak {S}$ of X, $k\in \mathbb {N},\eta \in (0,1)$ , and $t\in \mathbb {N}$ , define the set

$$ \begin{align*}Z(\mathfrak{S},k,t,\eta)\overset{\operatorname{def}}{=}\bigg\{A\in\mathbb{T}^{mn}:\frac{1}{k}\sum_{i=0}^{k-1}\delta_{a^{ti} x_A} (X\setminus \mathfrak{S})\ge\eta\bigg\}.\end{align*} $$

In other words, it is the set of $A\in \mathbb {T}^{mn}$ such that among $0,t, 2t, \ldots , (k-1)t$ , the proportion of times i for which the orbit point $a^{ti}x_A$ is in the complement of $\mathfrak {S}$ is at least $\eta $ . The following theorem is one of the main results in [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17].

Theorem 5.1. [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17, Theorem 1.5]

There exist $t_0>0$ and $C>0$ such that the following holds. For any $t>t_0$ , there exists a compact set $\mathfrak {S}=\mathfrak {S}(t)$ of X such that for any $k\in \mathbb {N}$ and $\eta \in (0,1)$ , the set $Z(\mathfrak {S},k,t,\eta )$ can be covered with $Ct^{3k}e^{(m+n-\eta )mntk}$ balls in $\mathbb {T}^{mn}$ of radius $e^{-(m+n)tk}$ .

Remark 5.2. Note that we can take $\mathfrak {S}(t)$ to be increasing in t, that is, $\mathfrak {S}(t) \subseteq \mathfrak {S}(t')$ for any $t_0<t\leq t'$ .

The following proposition is a slightly stronger variant of [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17, Theorem 1.1] which will be needed later. We prove this using Theorem 5.1.

Proposition 5.3. There exists a family of compact sets $\{\mathfrak {S}_\eta \}_{0<\eta < 1}$ of X such that the following is true. For any $0<\eta \leq 1$ ,

(5.1) $$ \begin{align} {\dim_H\bigg(\mathbb{T}^{mn}\setminus \limsup_{k\to\infty}\bigcap_{\eta'\ge\eta}F^k_{\eta',\mathfrak{S}_{\eta'}}\bigg)\leq mn-\frac{\eta mn}{2(m+n)}.} \end{align} $$

Proof. For $\eta \in (0,1)$ , let $t_\eta \ge 4$ be the smallest integer such that $({3\log t_\eta })/{t_\eta }\leq ({\eta mn}/{10})$ , and $\mathfrak {S}^{\prime }_\eta $ be the set $\mathfrak {S}(t_\eta )$ of Theorem 5.1. For $l\ge 4$ , denote by $\eta _l>0$ the smallest real number such that $t_{\eta _l}=l$ . Then, $\eta _l\ge ({3\eta _{l-1}}/{4})$ for any $l\ge 5$ . For $\eta '\in [\eta _{l},\eta _{l-1})$ , let us define $\mathfrak {S}^{\prime \prime }_{\eta '} = \mathfrak {S}^{\prime }_{\eta _l}$ . For any $\eta \in (0,1)$ , we set $\mathfrak {S}_\eta = \bigcup _{-t_{\eta }\leq t \leq t_\eta } a^t \mathfrak {S}_{\eta }"$ so that for any $-t_\eta \leq t\leq t_\eta $ and $x\in \mathfrak {S}^{\prime \prime }_\eta $ , $a^tx\in \mathfrak {S}_\eta $ .

Now we prove that this family of compact sets $\{\mathfrak {S}_\eta \}_{0<\eta <1}$ satisfies equation (5.1). Suppose $A\notin F_{\eta ,\mathfrak {S}_\eta }^k$ , which implies $({1}/{k})\sum _{i=0}^{k-1}\delta _{a^i x_A} (X\setminus \mathfrak {S}_\eta )\ge \eta $ . For sufficiently large k,

$$ \begin{align*}\frac{1}{\lceil{k}/{t_\eta}\rceil}\sum_{i=0}^{\lceil{k}/{t_\eta}\rceil-1}\delta_{a^{t_\eta i} x_A} (X\setminus \mathfrak{S}^{\prime\prime}_\eta)\ge \frac{1}{t_\eta \lceil{k}/{t_\eta}\rceil}\sum_{i=0}^{t_\eta(\lceil{k}/{t_\eta}\rceil-1)}\delta_{a^{i} x_A} (X\setminus \mathfrak{S}_\eta)\ge\frac{9}{10}\eta.\end{align*} $$

Hence, $\mathbb {T}^{mn}\setminus F_{\eta ,\mathfrak {S}_{\eta }}^{ k}\subseteq Z(\mathfrak {S}^{\prime \prime }_\eta ,\lceil {k}/{t_\eta }\rceil ,t_\eta ,({9}/{10})\eta )$ for any $0<\eta <1$ and sufficiently large $k\in \mathbb {N}$ .

For any $\eta _{l}< \eta '\leq \eta _{l-1}$ , we have $t_{\eta '}=l$ and the set $Z(\mathfrak {S}^{\prime \prime }_{\eta '},\lceil {k}/{t_{\eta '}}\rceil ,t_{\eta '},({9}/{10})\eta ')$ is contained in $Z(\mathfrak {S}^{\prime }_{\eta _l},\lceil {k}/{t_{\eta _l}}\rceil ,l,({9}/{10})\eta _l)$ . It follows that for any $0<\eta <1$ ,

$$ \begin{align*}\mathbb{T}^{mn}\setminus\bigcap_{\eta'\ge\eta}F_{\eta',\mathfrak{S}_{\eta'}^k}^{k}\subseteq \bigcup_{\eta'\ge\eta}Z\bigg(\mathfrak{S}^{\prime\prime}_{\eta'},\bigg\lceil\frac{k}{t_{\eta'}}\bigg\rceil,t_{\eta'},\frac{9}{10}\eta'\bigg)\subseteq \bigcup_{l=4}^{t_\eta}Z\bigg(\mathfrak{S}^{\prime}_{\eta_l},\bigg\lceil\frac{k}{l}\bigg\rceil,l,\frac{9}{10}\eta_l\bigg),\end{align*} $$

and hence

$$ \begin{align*}\mathbb{T}^{mn}\setminus\limsup_{k\to\infty}\bigcap_{\eta'\ge\eta}F^{k}_{\eta',\mathfrak{S}_{\eta'}}\subseteq \bigcup_{k_0\ge 1}\bigcap_{k=k_0}^{\infty}\bigcup_{l=4}^{t_\eta}Z\bigg(\mathfrak{S}^{\prime}_{\eta_l},\bigg\lceil\frac{k}{l}\bigg\rceil,l,\frac{9}{10}\eta_l\bigg).\end{align*} $$

By Theorem 5.1, the set $\bigcup _{l=4}^{t_\eta }Z(\mathfrak {S}^{\prime }_{\eta _l},\lceil {k}/{l}\rceil ,l,({9}/{10})\eta _l)$ can be covered with

$$ \begin{align*} \sum_{l=4}^{t_\eta}Cl^{3\lceil{k}/{l}\rceil}e^{(m+n-({9}/{10})\eta_l)mn\lceil{k}/{l}\rceil l}&\leq \sum_{l=4}^{t_\eta} Ct_\eta^3e^{({3\log l})/{l}k}e^{(m+n-({9}/{10})\eta_l)mn(k+t_\eta)}\\ &\leq\sum_{l=4}^{t_\eta}Ct_\eta^3e^{(m+n)mnt_\eta}e^{(m+n-({8}/{10})\eta_l)mnk}\\ &\leq Ct_\eta^4e^{(m+n)mnt_\eta}e^{(m+n-{\eta}/{2})mnk} \end{align*} $$

balls in $\mathbb {T}^{mn}$ of radius $e^{-(m+n)k}$ . Here, we used $\eta _{t_{\eta }}\ge ({3\eta }/{4})$ which follows from $\eta _l\ge ({3\eta _{l-1}}/{4})$ for any $l\ge 5$ . Thus, for any sufficiently large $k_0\in \mathbb {N}$ ,

$$ \begin{align*} \dim_H&\bigg(\bigcap_{k=k_0}^{\infty}\bigcup_{l=4}^{t_\eta}Z\bigg(\mathfrak{S}^{\prime}_{\eta_l},\bigg\lceil\frac{k}{l}\bigg\rceil,l,\eta_l\bigg)\bigg)\leq\limsup_{k\to\infty}\frac{\log(Ct_\eta^4e^{(m+n)mnt_\eta}e^{(m+n-{\eta}/{2})mnk})}{-\log(e^{-(m+n)k})}\\ &=\limsup_{k\to\infty}\frac{\log(Ct_\eta^4e^{(m+n)mnt_\eta})+(m+n-{\eta}/{2})mnk}{(m+n)k}=mn-{\eta mn}/{2(m+n)}, \end{align*} $$

and hence we get $\dim _H(\mathbb {T}^{mn}\setminus \limsup _{k\to \infty }\bigcap _{\eta '\ge \eta }F^{k}_{\eta ',\mathfrak {S}_{\eta '}})\leq mn-{\eta mn}/{2(m+n)}$ .

In the rest of this subsection, we will prove the following proposition which gives the bound of $\dim _H \mathbf {Bad}^b(\epsilon )$ . The construction of the a-invariant measure with large relative entropy roughly follows the construction in Proposition 4.1. However, the situation is significantly different, as fixing b does not determine the amount of excursion in the cusp. The additional step using Proposition 5.3 is necessary to control the measure near the cusp allowing a small amount of escape of mass.

Proposition 5.4. Let $\{\mathfrak {S}_\eta \}_{0<\eta < 1}$ be the family of compact sets of X as in Proposition 5.3. For fixed $b\in \mathbb {R}^m$ and $\epsilon>0$ , assume that $\dim _H \mathbf {Bad}^{b}(\epsilon )>\dim _H \mathbf {Bad}^{0}(\epsilon )$ . Let $\eta _0\overset {\operatorname {def}}{=}2(m+n)(1-({\dim _H \mathbf {Bad}^{b}(\epsilon )})/{mn})$ . Then, there exists an a-invariant measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ such that:

  1. (1) $\operatorname {Supp}{\overline {\mu }}\subseteq \mathcal {L}_\epsilon \cup (\overline {Y}\setminus Y)$ ;

  2. (2) $\pi _*\overline {\mu }(\overline {X}\setminus \mathfrak {S}_{\eta '})\leq \eta '$ for any $\eta _0\leq \eta '<1$ , in particular, there exist $\mu \in \mathscr {P}(Y)$ and $0\leq \widehat {\eta }\leq \eta _0$ such that

    $$ \begin{align*}\overline{\mu}=(1-\widehat{\eta})\mu+\widehat{\eta}\delta_\infty,\end{align*} $$
    where $\delta _\infty $ is the dirac delta measure on $\overline {Y}\setminus Y$ ;
  3. (3) let $\mathcal {A}^U$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=U$ , and let $\mathcal {A}^U_\infty $ be as in equation (2.12). Then, we have

    $$ \begin{align*}h_{\overline{\mu}}(a|\overline{\mathcal{A}^U_\infty})\ge(1-\widehat{\eta}^{{1}/{2}})\big(d-\tfrac{1}{2}\eta_0-d\widehat{\eta}^{{1}/{2}}\big).\end{align*} $$

Remark 5.5. We remark that this proposition is valid for the weighted setting except for the construction of $\{\mathfrak {S}_\eta \}_{0<\eta < 1}$ since it depends on the unweighted result (Theorem 5.1) in [Reference Kadyrov, Kleinbock, Lindenstrauss and MargulisKKLM17]. So, we keep the notation $\mathbf {r}$ and $\mathbf {s}$ for weights in the following proof.

Proof. For $\epsilon>0$ , denote by R the set $\mathbf {Bad}^{b}(\epsilon )\setminus \mathbf {Bad}_{0}^{b}(\epsilon )$ , and let

$$ \begin{align*}R^{T}\overset{\operatorname{def}}{=}\{A\in R \cap \mathbb{T}^{mn} \subset M_{m,n}(\mathbb{R}) |\text{ for all } t\ge T, a_t x_{A,b}\in \mathcal{L}_\epsilon\}.\end{align*} $$

The sequence $\{R^T\}_{T\ge 1}$ is increasing, and $R=\bigcup _{T=1}^\infty R^{T}$ by Proposition 3.2. Since $\dim _H \mathbf {Bad}^{b}(\epsilon )>\dim _H \mathbf {Bad}^{0}(\epsilon )\ge \dim _H \mathbf {Bad}_{0}^{b}(\epsilon )$ , it follows that $\dim _H R=\dim _H \mathbf {Bad}^{b}(\epsilon )$ . Thus, for any $\gamma>0$ , there exists $T_\gamma \ge 1$ satisfying

(5.2) $$ \begin{align} {\dim_H R^{T_\gamma}>\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma.} \end{align} $$

Let $\eta =2(m+n)(1-({\dim _H \mathbf {Bad}^{b}(\epsilon )-\gamma })/{mn})$ . If $0<\gamma <{mn}/{2(m+n)}-(mn-\dim _H \mathbf {Bad}^{b}(\epsilon ))$ , then $0<\eta <1$ . For $k\in \mathbb {N}$ , write $\widetilde {F}_\eta ^k\overset {\operatorname {def}}{=}\bigcap _{\eta '\ge \eta }F^k_{\eta ',\mathfrak {S}_{\eta '}}$ for simplicity. Recall that we have

(5.3) $$ \begin{align} {\dim_H (\mathbb{T}^{mn}\setminus\limsup_{k\to\infty}\widetilde{F}_\eta^k) \leq mn-\frac{\eta mn}{2(m+n)}=\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma} \end{align} $$

by Theorem 5.3. It follows from equations (5.2) and (5.3) that

$$ \begin{align*}\dim_H\Big(R^{T_\gamma}\cap \limsup_{k\to\infty}\widetilde{F}_\eta^k\Big)>\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma.\end{align*} $$

Thus, there is an increasing sequence of positive integers $\{k_i\}\to \infty $ such that

$$ \begin{align*}\dim_H (R^{T_\gamma}\cap \widetilde{F}_{\eta}^{k_i})> \dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma.\end{align*} $$

For each $k_i\ge T_\gamma $ , let $S_i$ be a maximal $e^{-k_i}$ -separated subset of $R^{T_\gamma }\cap \widetilde {F}_{\eta }^{k_i}$ with respect to the quasi-distance $d_{\mathbf {r}\otimes \mathbf {s}}$ . By Lemma 3.1,

(5.4) $$ \begin{align} \liminf_{i\to\infty}\frac{\log |S_i|}{k_i} \ge\underline{\dim}_{\mathbf{r}\otimes\mathbf{s}} (R^{T_\gamma}\cap \widetilde{F}_\eta^{k_i}) &> m+n-(r_1+s_1)(mn-\dim_H \mathbf{Bad}^{b}(\epsilon)+\gamma)\nonumber\\&= m+n-\frac{m+n}{mn}(mn-\dim_H \mathbf{Bad}^{b}(\epsilon)+\gamma) \nonumber\\&=\frac{m+n}{mn}(\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma). \end{align} $$

Let $\nu _i\overset {\operatorname {def}}{=} ({1}/{|S_i|})\sum _{y\in D_i}\delta _{y}= ({1}/{|S_i|})\sum _{A\in S_i}\delta _{y_{A,b}}$ be the normalized counting measure on the set $D_i\overset {\operatorname {def}}{=}\{y_{A,b}:A\in S_i\}\subset Y$ and let $\mu ^{\gamma }$ be a weak*-limit of $\mu _i$ :

$$ \begin{align*}\mu_i\overset{\operatorname{def}}{=} \frac{1}{k_i}\sum_{k=0}^{k_i-1}a^k_{*}\nu_i \overset{\operatorname{w}^*}{\longrightarrow} \mu^{\gamma}\in\mathscr{P}(\overline{Y}).\end{align*} $$

By extracting a subsequence if necessary, we may assume that $\mu ^{\gamma }$ is a weak*-accumulation point of $\{\mu _i\}$ . The measure $\mu ^{\gamma }$ is clearly an a-invariant measure since $a_*\mu _i-\mu _i$ goes to zero measure.

Choose any sequence of positive real numbers $(\gamma _j)_{j\ge 1}$ converging to zero and $(\eta _j)_{j\ge 1}$ be the corresponding sequence such that

$$ \begin{align*}\eta_j=2(m+n)\bigg(1-\frac{\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma_j}{mn}\bigg).\end{align*} $$

Let $\{\mu ^{\gamma _j}\}$ be a family of a-invariant probability measures on $\overline {Y}$ obtained from the above construction for each $\gamma _j$ . Extracting a subsequence again if necessary, we may take a weak $^*$ -limit measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ of $\{\mu ^{\gamma _j}\}$ . We prove that $\overline {\mu }$ is the desired measure. The measure $\overline {\mu }$ is clearly a-invariant.

(1) We show that for any $\gamma $ , $\mu ^\gamma (Y\setminus \mathcal {L}_\epsilon )=0$ . For any $A\in S_i\subseteq R^{T_\gamma }$ , $a^T y_{A,b}\in \mathcal {L}_\epsilon $ holds for $T>T_\gamma $ . Thus,

$$ \begin{align*}\mu_i(Y\setminus\mathcal{L}_\epsilon)=\frac{1}{k_i}\sum_{k=0}^{k_i-1}(a^k)_{*}\nu_i(Y\setminus\mathcal{L}_\epsilon)=\frac{1}{k_i}\sum_{k=0}^{T_\gamma}(a^k)_{*}\nu_i(Y\setminus\mathcal{L}_\epsilon)\leq\frac{T_\gamma}{k_i}.\end{align*} $$

By taking the limit for $k_i\to \infty $ , we have $\mu ^\gamma (Y\setminus \mathcal {L}_\epsilon )=0$ for arbitrary $\gamma $ , and hence,

$$ \begin{align*}\overline{\mu}(Y\setminus\mathcal{L}_\epsilon)=\lim_{j\to\infty}\mu^{\gamma_j}(Y\setminus\mathcal{L}_\epsilon)=0.\end{align*} $$

(2) For any $\gamma =\gamma _j$ , if $A\in S_i\subset \widetilde {F}_{\eta _j}^{k_i}=\bigcap _{\eta '\ge \eta _j}F_{\eta ',\mathfrak {S}_{\eta '}}^{k_i}$ , then for all $i \in \mathbb {N}$ and $\eta _j \leq \eta '\leq 1$ , $({1}/{k_i})\sum _{k=0}^{k_i-1}\delta _{a^k x_A}(X\setminus \mathfrak {S}_{\eta '})<\eta '$ . Therefore, for all $i\in \mathbb {N}$ and $\eta _j \leq \eta '\leq 1$ ,

$$ \begin{align*} \pi_*\mu_i(X\setminus \mathfrak{S}_{\eta'}) &=\frac{1}{|S_i|}\sum_{A\in S_i} \frac{1}{k_i}\sum_{k=0}^{k_i-1} \delta_{a^k x_A}(X\setminus \mathfrak{S}_{\eta'}) <\eta', \end{align*} $$

and hence $\pi _*\mu ^{\gamma _j}(\overline {X}\setminus \mathfrak {S}_{\eta '})=\lim _{i\to \infty }\pi _*\mu _i(X\setminus \mathfrak {S}_{\eta '})\leq \eta '$ . Since $\eta _j$ converges to $\eta _0$ as $j\to \infty $ , we have

$$ \begin{align*}\pi_*\overline{\mu}(\overline{X}\setminus \mathfrak{S}_{\eta'})\leq \eta'\end{align*} $$

for any $\eta ' \geq \eta _0$ . Hence,

$$ \begin{align*}\overline{\mu}(\overline{Y}\setminus Y)\leq \lim_{\eta'\to\eta_0}\pi_*\overline{\mu}(\overline{X}\setminus \mathfrak{S}_{\eta'})\leq \eta_0,\end{align*} $$

so we have a decomposition $\overline {\mu }=(1-\widehat {\eta })\mu +\widehat {\eta }\delta _\infty $ for some $\mu \in \mathscr {P}(Y)$ and $0\leq \widehat {\eta }\leq \eta _0$ .

For the rest of the proof, let us check the condition (3).

(3) We first fix any $D>J_U =m+n$ . As in the proof of Proposition 4.1, there exists a finite partition $\mathcal {Q}$ of Y satisfying:

  • $\mathcal {Q}$ contains an atom $Q_\infty $ of the form $\pi ^{-1}(Q_\infty ^0)$ , where $X\smallsetminus Q_\infty ^0$ has compact closure;

  • $\text { for all } Q\in \mathcal {Q}\smallsetminus \{Q_\infty \}$ , $\operatorname {\mathrm {diam}} Q<r_D=r_D(Q_\infty ^0)$ , where $r_D$ is as in §3.3;

  • $\text { for all } Q\in \mathcal {Q},\text { for all } j\geq 1,\; \mu ^{\gamma _j}(\partial Q)=0$ .

Remark that for all $i\geq 1$ , $D_i \subset \{y_{A,b}:A\in [0,1]^{mn}, b\in [0,1]^m\}$ , which is a compact set in Y; therefore we can choose $Q^0_{\infty }$ so that

(5.5) $$ \begin{align} { Q_\infty \cap D_i = \varnothing. } \end{align} $$

To prove condition (3), it suffices to prove that for all $q\ge 1$ ,

(5.6) $$ \begin{align} {\frac{1}{q}H_{\overline{\mu}}(\overline{\mathcal{Q}}^{(q)}|\overline{\mathcal{A}^U_\infty}) \ge (1-\overline{\mu}(\overline{Q_\infty})^{{1}/{2}})\bigg(\frac{m+n}{mn}\dim_H \mathbf{Bad}^{b}(\epsilon) -D\overline{\mu}(\overline{Q_\infty})^{{1}/{2}}\bigg).} \end{align} $$

Indeed, taking $D\to m+n$ and $Q^0_\infty \subset X$ such that $\overline {\mu }(\overline {Q_\infty }) \to \widehat {\eta }$ , it follows that

$$ \begin{align*} h_{\overline{\mu}}(a|\overline{\mathcal{A}^U_\infty})&\ge(m+n)(1-\widehat{\eta}^{{1}/{2}})\bigg(\frac{1}{mn}\dim_H \mathbf{Bad}^{b}(\epsilon)-\widehat{\eta}^{{1}/{2}}\bigg)\\ &=(1-\widehat{\eta}^{{1}/{2}})\bigg(d-\frac{1}{2}\eta_0-d\widehat{\eta}^{{1}/{2}}\bigg). \end{align*} $$

It remains to prove equation (5.6). It is trivial if $\overline {\mu }(\overline {Q_\infty })=1$ , so assume that $\overline {\mu }(\overline {Q_\infty })<1$ , and hence for all large enough $j\ge 1$ , $\mu ^{\gamma _j}(\overline {Q_\infty })<1$ . Now we fix such $j\ge 1$ and write temporarily $\gamma =\gamma _j$ .

Choose $\beta>0$ such that $\mu ^\gamma (\overline {Q_\infty }) < \beta < 1$ . Then, for large enough i,

$$ \begin{align*}\mu_i(Q_\infty)=\frac{1}{k_i|S_i|}\sum_{y\in D_i, 0\le k<k_i}\delta_{a^k y}(Q_\infty) < \beta.\end{align*} $$

In other words, there exist at most $\beta k_i|S_i|$ number of $a^k y$ terms in $Q_\infty $ with $y\in D_i$ and $0\leq k<k_i$ .

Let $S^{\prime }_i\subset S_i$ be the set of $A\in S_i$ terms such that

(5.7) $$ \begin{align} {|\{0\leq k< k_i: a^ky_{A,b} \in Q_\infty \} |\leq \beta^{{1}/{2}}k_i.} \end{align} $$

Thus, we have $|S_i\setminus S_i'|\leq \beta ^{1/2}|S_i|$ , and hence

(5.8) $$ \begin{align} {|S^{\prime}_i|\ge (1-\beta^{{1}/{2}})|S_i|.} \end{align} $$

Let $\nu ^{\prime }_i\overset {\operatorname {def}}{=} ({1}/{|S^{\prime }_i|})\sum _{y\in S^{\prime }_i}\delta _y$ be the normalized counting measure on $D^{\prime }_i$ , where $D^{\prime }_i\overset {\operatorname {def}}{=}\{y_{A,b}:A\in S^{\prime }_i\}\subset Y$ . By definition, $\nu _i(Q)\ge {|S^{\prime }_i|}/{|S_i|}\nu ^{\prime }_i(Q)$ for all measurable set $Q\subseteq Y$ . Thus,

(5.9) $$ \begin{align} H_{\nu_i}(\mathcal{Q})&=-\sum_{\nu_i(Q)\leq{1}/{e}}\log(\nu_i(Q))\nu_i(Q)-\sum_{\nu_i(Q)>{1}/{e}}\log(\nu_i(Q))\nu_i(Q)\nonumber\\&\ge -\sum_{\nu_i(Q)\leq{1}/{e}}\log\bigg(\frac{|S^{\prime}_i|}{|S_i|}\nu^{\prime}_i(Q)\bigg)\frac{|S^{\prime}_i|}{|S_i|}\nu^{\prime}_i(Q) \nonumber\\&=-\frac{|S^{\prime}_i|}{|S_i|}\sum_{\nu_i(Q)\leq{1}/{e}}\log(\nu^{\prime}_i(Q))\nu^{\prime}_i(Q)-\frac{|S^{\prime}_i|}{|S_i|}\log{\frac{|S^{\prime}_i|}{|S_i|}}\sum_{\nu_i(Q)\leq{1}/{e}}\nu^{\prime}_i(Q) \nonumber\\&\geq \frac{|S^{\prime}_i|}{|S_i|}\bigg\{H_{\nu^{\prime}_i}(\mathcal{Q})+\sum_{\nu_i(Q)>{1}/{e}}\log(\nu^{\prime}_i(Q))\nu^{\prime}_i(Q)\bigg\}\nonumber\\&\ge (1-\beta^{{1}/{2}})\bigg(H_{\nu^{\prime}_i}(\mathcal{Q})-\frac{2}{e}\bigg). \end{align} $$

In the last inequality, we use the fact that $\nu ^{\prime }_i$ is a probability measure, and thus there can be at most two elements Q of the partition for which $\nu ^{\prime }_i (Q)> {1}/{e}$ .

To compute $H_{\nu _i'}(\mathcal {Q}^{(k_i)})$ , note that for any $y \in D_i'$ , $y \notin Q_\infty $ . From Lemma 3.4 with $L=U$ , equations (5.5) and (5.7), if $Q \neq Q_\infty $ is any non-empty atom of $\mathcal {Q}^{(k_i)}$ , fixing any $y\in D^{\prime }_{i}\cap Q$ , the set

$$ \begin{align*} { D^{\prime}_{i}\cap Q = D^{\prime}_{i}\cap [y]_{\mathcal{Q}^{(k_i)}} \subset E_{y,k_{i}-1} } \end{align*} $$

can be covered by $Ce^{D\sqrt {\beta } k_{i}} d_{\mathbf {r}\otimes \mathbf {s}}$ -balls of radius $r_D^{{1}/({r_1 +s_1})}e^{-k_{i}}$ , where C is a constant depending on $Q^0_\infty $ and D, but not on $k_i$ . Since $D^{\prime }_{i}$ is $e^{-k_{i}}$ -separated with respect to $d_{\mathbf {r}\otimes \mathbf {s}}$ and $r_D^{{1}/({r_1 +s_1})}<\tfrac 12$ , we get

$$ \begin{align*}|S_i'|\nu_i'(Q) = \mathrm{Card}(D^{\prime}_i \cap Q)\leq Ce^{D\sqrt{\beta} k_i },\end{align*} $$

and hence we have

(5.10) $$ \begin{align} { H_{\nu_i'}(\mathcal{Q}^{(k_i)})\ge\log |S_i'|-D\beta^{{1}/{2}} k_i-\log C.} \end{align} $$

Now let $\mathcal {A}^U=(\mathcal {P}^U)_0^\infty =\bigvee _{i=0}^{\infty }a^i \mathcal {P}^U$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=U$ , and let $\mathcal {A}^U_\infty $ be as in equation (2.12).

Claim. $H_{\nu _i}(\mathcal {Q}^{(k_i)}|\mathcal {A}^U_\infty ) = H_{\nu _i}(\mathcal {Q}^{(k_i)})$ .

Proof of the claim

Using the continuity of entropy, we have

$$ \begin{align*} {H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^U_\infty)=\lim_{\ell\to\infty}H_{\nu_i}(\mathcal{Q}^{(k_i)}|(\mathcal{P}^U)_\ell^\infty).} \end{align*} $$

Now we show $H_{\nu _i}(\mathcal {Q}^{(k_i)}|(\mathcal {P}^U)_\ell ^\infty ) = H_{\nu _i}(\mathcal {Q}^{(k_i)})$ for all large enough $\ell \geq 1$ . Let $\mathcal {P}$ and $E_{\delta }$ be as in Lemma 2.7 for $\mu $ and $r_0$ . As mentioned in Remark 2.4, we may assume that there exists $y\in \{y_{A,b}:A\in \mathbb {T}^{mn}\subset M_{m,n}(\mathbb {R})\}$ such that $y\notin \partial \mathcal {P}$ . Since $E_\delta = \bigcup _{k=0}^\infty a^k \partial _{d_0e^{-k\alpha }\delta }\mathcal {P}$ , $y\in Y \setminus E_{\delta }$ for some small enough $\delta>0$ , which implies that $a^{-\ell }y\in Y\setminus a^{-\ell }E_\delta \subset Y\setminus E_\delta $ . Hence, it follows from equation (2.6) and Proposition 2.8 that

$$ \begin{align*} [y]_{(\mathcal{P}^U)_\ell^\infty} = a^\ell[a^{-\ell}y]_{(\mathcal{P}^U)_0^\infty} = a^\ell[a^{-\ell}y]_{\mathcal{A}^U} \supset a^{\ell} B_{\delta}^{U} a^{-\ell} y \supset B_{d_0 e^{\alpha\ell}\delta}^{U} y. \end{align*} $$

Since the support of $\nu _i$ is a set of finite points on a single compact U-orbit, $\nu _i$ is supported on a single atom of $(\mathcal {P}^U)_\ell ^\infty $ for all large enough $\ell \geq 1$ . This proves the claim.

Combining equations (5.8)–(5.10), and the above claim, we have

(5.11) $$ \begin{align} H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^U_\infty)&= H_{\nu_i}(\mathcal{Q}^{(k_i)}) \ge (1-\beta^{{1}/{2}})\bigg(H_{\nu_i'}(\mathcal{Q}^{(k_i)})-\frac{2}{e}\bigg)\nonumber\\ &\ge (1-\beta^{{1}/{2}})\bigg(\log |S_i|-D\beta^{{1}/{2}} k_i-\log C-\frac{2}{e}+\log(1-\beta^{{1}/{2}})\bigg). \end{align} $$

As in equation (4.5), it follows from equation (5.11) that

$$ \begin{align*} \frac{1}{q}&H_{\mu_i}(\mathcal{Q}^{(q)}|\mathcal{A}^U_\infty) \ge \frac{1}{k_i}H_{\nu_i}(\mathcal{Q}^{(k_i)}|\mathcal{A}^U_\infty)-\frac{2q\log |\mathcal{Q}|}{k_i}\\ &\ge \frac{1}{k_i}\bigg((1-\beta^{{1}/{2}})\bigg(\log|S_i|-D\beta^{{1}/{2}}k_i-\log{C}-\frac{2}{e}+\log(1-\beta^{{1}/{2}})\bigg) -2q\log|\mathcal{Q}|\bigg). \end{align*} $$

Now we can take $i\to \infty $ because the atoms Q of $\mathcal {Q}$ and hence of $\mathcal {Q}^{(q)}$ satisfy $\mu ^\gamma (\partial Q)=0$ . Also, the constants C, $\beta $ , and $|\mathcal {Q}|$ are independent of $k_i$ . Thus, it follows from the inequality in equation (5.4) that

$$ \begin{align*} \frac{1}{q}H_{\mu^\gamma}(\overline{\mathcal{Q}}^{(q)}|\overline{\mathcal{A}^U_\infty}) &\ge (1-\beta^{{1}/{2}})\bigg(\frac{m+n}{mn}(\dim_H \mathbf{Bad}^{b}(\epsilon)-\gamma)-D\beta^{{1}/{2}}\bigg). \end{align*} $$

By taking $\beta \to \overline {\mu }(\overline {Q_\infty })$ and $\gamma =\gamma _j \to 0$ , the inequality in equation (5.6) follows.

5.2 Effective equidistribution and the proof of Theorem 1.1

In this subsection, we recall some effective equidistribution results which are necessary for the proof of Theorem 1.1. Let $\mathfrak {g}=\operatorname {Lie}\,G(\mathbb {R})$ and choose an orthonormal basis for $\mathfrak {g}$ . Define the (left) differentiation action of $\mathfrak {g}$ on $C_c^\infty (X)$ by $Zf(x)={df}(\textrm {exp}(tZ)x)/{dt}|_{t=0}$ for $f\in C_c^\infty (X)$ and Z in the orthonormal basis. This also defines for any $l\in \mathbb {N}$ , $L^2$ -Sobolev norms $\mathcal {S}_l$ on $C^\infty _c(Y)$ :

$$ \begin{align*} {\mathcal{S}_l(f)^2\overset{\operatorname{def}}{=}\sum_{\mathcal{D}}\|\textrm{ht}\circ \pi ^{l}\mathcal{D}(f)\|^2_{L^2},} \end{align*} $$

where $\mathcal {D}$ ranges over all the monomials in the chosen basis of degree $\leq l$ and $\textrm {ht } \circ \pi $ is the function assigning 1 over the smallest length of a vector in the lattice corresponding to the given grid. Let us define the function $\zeta : (\mathbb {T}^{d}\setminus \mathbb {Q}^d)\times \mathbb {R}^{+}\to \mathbb {N}$ measuring the Diophantine property of b:

$$ \begin{align*} { \zeta(b,T)\overset{\operatorname{def}}{=} \min\bigg\{N\in\mathbb{N} : \min_{1\leq q\leq N}\|qb\|_{\mathbb{Z}}\leq\frac{T^2}{N} \bigg\}. } \end{align*} $$

Then there exists a sufficiently large $l\in \mathbb {N}$ such that the following equidistribution theorems hold.

Theorem 5.6. [Reference KimKim, Theorem 1.3]

Let K be a bounded subset in $\operatorname {SL}_d(\mathbb {R})$ and $V\subset U$ be a fixed neighborhood of the identity in U with smooth boundary and compact closure. Then, for any $t\ge 0$ , $f\in C_c^\infty (Y)$ , and $y=gw(b)\Gamma $ with $g\in K$ and $b\in \mathbb {T}^d\setminus \mathbb {Q}^d$ , there exists a constant $\alpha _1>0$ depending only on d and V so that

(5.12) $$ \begin{align} {\frac{1}{m_U(V)}\int_V f(a_tuy)\,dm_U(u)=\int_Y fdm_Y+O(\mathcal{S}_l(f)\zeta(b,e^{{t}/{2m}})^{-\alpha_1}).} \end{align} $$

The implied constant in equation (5.12) depends only on d, V, and K.

For $q\in \mathbb {N}$ , define

$$ \begin{align*} X_q &\overset{\operatorname{def}}{=} \{gw(\mathbf{p}/q)\Gamma\in Y : g\in \operatorname{SL}_{d}(\mathbb{R}), \mathbf{p}\in\mathbb{Z}^d, \gcd(\mathbf{p},q)=1\},\\ \Gamma_q &\overset{\operatorname{def}}{=} \{\gamma\in SL_{d}(\mathbb{Z}): \gamma e_1 \equiv e_1 \;(\bmod\; q)\}. \end{align*} $$

Lemma 5.7. The subspace $X_q\subset Y$ can be identified with the quotient space $\operatorname {SL}_d(\mathbb {R})/\Gamma _{q}$ . In particular, this identification is locally bi-Lipschitz.

Proof. The action $\operatorname {SL}_d(\mathbb {R})$ on $X_q$ by the left multiplication is transitive and $\operatorname {Stab}_{\operatorname {SL}_d(\mathbb {R})} (w(e_1 /q)\Gamma )=\Gamma _q$ . To see the transitivity, it is enough to show the transitivity on each fiber, that is,

$$ \begin{align*}\operatorname{SL}_d(\mathbb{Z})e_1 \equiv \{\mathbf{p}\in\mathbb{Z}^{d}: \gcd(\mathbf{p},q)=1\} \;(\bmod\; q).\end{align*} $$

Write $D=\gcd (\mathbf {p})$ and $\mathbf {p}'=\mathbf {p}/D$ . Since $\gcd (D,q)=1$ , there are $a,b\in \mathbb {Z}$ such that $aD+bq=1$ . Take $A\in M_{d,d}(\mathbb {Z})$ such that $\det (A)=D$ and $Ae_1=\mathbf {p}$ . If we set $\mathbf {u}=b\mathbf {p}'+(a-1)Ae_2$ , then by direct calculation, we have $\mathbf {p}+q\mathbf {u}=(A+\mathbf {u}\times {^{t}}(qe_1 + e_2))e_1$ and $A+\mathbf {u}\times {^{t}}(qe_1 + e_2)\in \operatorname {SL}_d(\mathbb {Z})$ , which concludes the transitivity. Bi-Lipshitz property of the identification follows trivially since both $X_q$ and $\operatorname {SL}_d(\mathbb {R})/\Gamma _q$ are locally isometric to $\operatorname {SL}_d(\mathbb {R})$ .

Theorem 5.8. [Reference Kleinbock, Margulis, Goldfeld, Jorgenson, Jones, Ramakrishnan, Ribet and TateKM12, Theorem 2.3]

For $q\in \mathbb {N}$ , let $\operatorname {SL}_d(\mathbb {R})/\Gamma _q\simeq X_q\subset Y$ . Let K and V be as in Theorem 5.6. Then, for any $t\ge 0$ , $f\in C_c^\infty (Y)$ , and $y=gw({\mathbf {p}}/{q})\Gamma $ with $g\in K$ and $\mathbf {p}\in \mathbb {Z}^d$ , there exists a constant $\alpha _2>0$ depending only on d and V so that

(5.13) $$ \begin{align} {\frac{1}{m_U(V)}\int_V f(a_tuy)\,dm_U(u)=\int_{X_q} fdm_{X_q}+O(\mathcal{S}_l(f)[\Gamma_1:\Gamma_q]^{{1}/{2}}e^{-\alpha_2 t}).} \end{align} $$

The implied constant in equation (5.13) depends only on d, V, and K.

Proof. This result was obtained in [Reference Kleinbock, Margulis, Goldfeld, Jorgenson, Jones, Ramakrishnan, Ribet and TateKM12, Theorem 2.3] in the case $q=1$ . For general q, we refer the reader to [Reference Khalil and LuethiKM23, Theorem 5.4] which gave a sketch of the required modification. [Reference Khalil and LuethiKM23, Theorem 5.4] is actually stated for different congruence subgroups from our $\Gamma _q$ , but the modification still works.

Since we assume the unweighted setting, $\mathcal {L}_{\epsilon }=\{y\in Y : \text {for all } v\in \Lambda _{y},\ \|v\|\geq \epsilon ^{1/d} \}$ .

Lemma 5.9. For any small enough $\epsilon>0$ and $q\in \mathbb {N}$ , $m_Y(Y_{\leq \epsilon ^{-1}}\setminus \mathcal {L}_\epsilon )\asymp \epsilon $ and $m_{X_q}(Y_{\leq \epsilon ^{-1}}\setminus \mathcal {L}_\epsilon )\gg q^{-d}\epsilon $ .

Proof. Using the Siegel integral formula [Reference Margulis and MohammadiMM11, Lemma 2.1] with , which is the indicator function on $\epsilon ^{1/d}$ -ball centered at $0$ in $\mathbb {R}^d$ , we have $m_Y(Y_{\leq \epsilon ^{-1}}\setminus \mathcal {L}_\epsilon )\ll \epsilon $ . However, by [Reference AthreyaAth15, Theorem 1] with $A=B_{\epsilon ^{1/d}}(0)$ , we have $m_{Y}(\mathcal {L}_\epsilon )< {1}/({1+2^d \epsilon })$ . It follows from the Siegel integral formula on X that $m_{Y}(Y_{>\epsilon ^{-1}})=m_{X}(X_{>\epsilon ^{-1}})\leq 2^d\epsilon ^d$ . Since $d\geq 2$ , we have

$$ \begin{align*} { m_Y (Y_{\leq\epsilon^{-1}}\setminus\mathcal{L}_\epsilon) \geq m_Y (Y\setminus\mathcal{L}_\epsilon)-m_Y (Y_{>\epsilon^{-1}}) > \frac{2^d \epsilon}{1+2^d \epsilon}-2^d \epsilon^d \gg \epsilon } \end{align*} $$

for small enough $\epsilon>0$ , which concludes the first assertion.

To prove the second assertion, observe that for any $x{\kern-1pt}\in{\kern-1pt} X_{>\epsilon ^{-1/d}}$ , there exists $g{\kern-1pt}\in{\kern-1pt} \operatorname {SL}_d(\mathbb {R})$ such that $x=g\operatorname {SL}_d(\mathbb {Z})$ and $\|ge_1\|\leq \epsilon ^{1/d}$ . Then, $gw({e_1}/{q})\Gamma \in \pi _{q}^{-1}(x)\cap (Y\setminus \mathcal {L}_\epsilon )$ , where $\pi _q : X_q \to X$ is the natural projection. Since $|\pi _{q}^{-1}(x)|\leq q^{d}$ and $m_X (x\in X: \epsilon ^{-1/d}<\textrm {ht}(x)\leq \epsilon ^{-1}) \asymp \epsilon $ , we have

$$ \begin{align*} m_{X_q}(Y_{\leq\epsilon^{-1}}\setminus\mathcal{L}_\epsilon) \geq \frac{|\pi_{q}^{-1}(x)\cap (Y\setminus\mathcal{L}_\epsilon)|}{|\pi_{q}^{-1}(x)|} m_X (x\in X: \epsilon^{-1/d}<\textrm{ht}(x)\leq \epsilon^{-1}) \gg q^{-d}\epsilon.\\[-30pt] \end{align*} $$

Proposition 5.10. Let $\mathcal {A}$ be a countably generated sub- $\sigma $ -algebra of the Borel $\sigma $ -algebra which is $a^{-1}$ -descending and U-subordinate. Fix a compact set $K\subset Y$ . Let $1<R'<R$ , $k=\lfloor ({mn\log R'})/{4d}\rfloor $ . Suppose that $y\in a^{4k}K$ satisfies $B^{U,d_\infty }_{R'}\cdot y\subset [y]_{\mathcal {A}}\subset B^{U,d_\infty }_{R}\cdot y$ , where $B^{U,d_\infty }_r$ is the $d_\infty $ -ball of radius r around the identity in U. For $\epsilon>0$ , let $\Omega \subset Y$ be a set satisfying $\Omega \cup a^{-3k}\Omega \subseteq \mathcal {L}_{{\epsilon }/{2}}$ . There exist $M,M'>0$ such that the following holds. If $R'\ge \epsilon ^{-M'}$ , then

$$ \begin{align*}1-\tau^{\mathcal{A}}_y(\Omega)\gg \bigg(\frac{R'}{R}\bigg)^{mn}\epsilon^{dM+1},\end{align*} $$

where the implied constant depends only on K.

Proof. Denote by $V_y\subset U$ the shape of $\mathcal {A}$ -atom of y so that $V_y\cdot y=[y]_{\mathcal {A}}$ . Set $V=B^{U,d_\infty }_{1}$ . Since $({mn\log R'})/{d}-4\leq 4k\leq ({mn\log R'})/{d}$ , we have

$$ \begin{align*}B^{U,d_\infty}_{e^{-{4d}/{mn}}R'} \subseteq a^{4k}Va^{-4k} = B_{e^{{d}/{mn}4k}}^{U,d_\infty}\subseteq B_{R'}^{U,d_\infty} \subseteq V_y.\end{align*} $$

It follows that

It remains to show that

(5.14)

We will approximate the characteristic function in the above integrand by a smooth function $\psi $ and use effective equidistribution results from Theorems 5.6 and 5.8. Since $\pi (K) \subset X$ is compact, we can choose $g_0 \in SL_d(\mathbb {R})$ such that $\|g_0\|<C_K$ with a constant $C_K>0$ depending only on K, and $a^{-4k}y=g_0w(b_0)\Gamma $ with $b_0 \in \mathbb {R}^d$ . For the constants $\alpha _1$ in Theorem 5.6 and $\alpha _2$ in Theorem 5.8, let $\alpha =\min (\alpha _1,\alpha _2)$ and $M=({1}/{\alpha })(2+l+({\dim G})/{2d})$ . By [Reference Kleinbock and MargulisKM96, Lemma 2.4.7(b)] with $r=C\epsilon ^{{1}/{d}}<1$ , we can take the approximation function $\theta \in C_{c}^{\infty }(G)$ of the identity such that $\theta \ge 0$ , $\operatorname {Supp} \theta \subseteq B^{G}_r(\mathrm {id})$ , $\int _G \theta =1$ , and $\mathcal {S}_l(\theta )\ll \epsilon ^{-({1}/{d})(l+({\dim G})/{2})}$ . Let , then we have . Moreover, using Young’s inequality, its Sobolev norm is bounded as follows:

and hence $\mathcal {S}_l(\psi )\ll \epsilon ^{-{l}/{2}}\mathcal {S}_l(\theta )\leq \epsilon ^{-(l+({\dim G})/{2d})}$ .

We will prove equation (5.14) applying Theorems 5.6 and 5.8 to the following two cases, respectively:

$$ \begin{align*} \textit{Case (i)}\quad \zeta(b_0,e^{{2k}/{m}})\ge\frac{r_0}{C_K C_0}\epsilon^{-M}\quad\text{and}\quad \textit{Case (ii)}\quad \zeta(b_0,e^{{2k}/{m}})<\frac{r_0}{C_K C_0}\epsilon^{-M}.\quad \end{align*} $$

Case (i): Applying Theorem 5.6, we have

It follows from Lemma 5.9 and $M\alpha =2+(l+({\dim G})/{2d})$ that

Case (ii): The assumption $\zeta (b_0,e^{{2k}/{m}})<({r_0}/{C_K C_0})\epsilon ^{-M}$ implies that there exists $q\leq ({r_0}/{C_K C_0}) \epsilon ^{-M}$ such that $\|qb_0\|_{\mathbb {Z}}\leq q^{2}e^{-{2k}/{m}}$ , whence

(5.15) $$ \begin{align} {\bigg\|b_0-\frac{\mathbf{p}}{q}\bigg\|\leq qe^{-{2k}/{m}}\leq \frac{r_0}{C_K C_0}\epsilon^{-M}e^{-{2k}/{m}}} \end{align} $$

for some $\mathbf {p}\in \mathbb {Z}^d$ . Let $y'=a^{4k}g_0w({\mathbf {p}}/{q})\Gamma $ . Then, for any $u\in V$ ,

$$ \begin{align*} &d_Y(a^kua^{-4k}y, a^kua^{-4k}y')\\ &\quad\leq d_G\bigg(a^k u g_0 w(b_0), a^k u g_0 w\bigg(\frac{\mathbf{p}}{q}\bigg)\bigg)=d_G \left(\left(\begin{matrix} I_d & a^k u g_0 \bigg(b_0 - \dfrac{\mathbf{p}}{q}\bigg) \\ & 1 \\ \end{matrix}\right), \mathrm{id}\right)\\ &\quad \leq C_0 d_\infty \left(\left(\begin{matrix} I_d & a^k u g_0 \bigg(b_0 - \dfrac{p}{q}\bigg) \\ & 1 \\ \end{matrix}\right), \mathrm{id}\right) \leq C_0 e^{{k}/{m}} \|g_0\| \bigg\|b_0-\frac{\mathbf{p}}{q}\bigg\| \leq r_0 \epsilon^{-M}e^{-{k}/{m}} \end{align*} $$

by equations (3.1) and (5.15). Hence, we have

(5.16) $$ \begin{align} |\psi(a^kua^{-4k}y)-\psi(a^kua^{-4k}y')|&\ll\mathcal{S}_l(\psi)d_Y(a^kua^{-4k}y,a^kua^{-4k}y')\ll \mathcal{S}_l(\psi)\epsilon^{-M}e^{-{k}/{m}}. \end{align} $$

It follows from the assumption $a^{-3k}\Omega \subseteq \mathcal {L}_{{\epsilon }/{2}}$ , equation (5.16), and Theorem 5.8 that

Let $M'{\kern-1.5pt}={\kern-1.7pt}\min ({4d}/{\alpha }(l{\kern-1.5pt}+{\kern-1.5pt}({\dim{\kern-1pt} G})/{2d}{\kern-1.5pt}+{\kern-1.5pt}{3dM}/{2}{\kern-1.5pt}+{\kern-1.5pt}2), 4dm(l{\kern-1.5pt}+{\kern-1.5pt}({\dim{\kern-1pt} G})/{2d}{\kern-1.5pt}+{\kern-1.5pt}(d{\kern-1.5pt}+{\kern-1.5pt}1)M{\kern-1.5pt}+{\kern-1.5pt}2))$ . If $R'>\epsilon ^{-M'}$ , then $e^{-4dk}<e^{4d}\epsilon ^{M'}$ , so $\epsilon ^{-(l+({\dim G})/{2d})-{dM}/{2}}e^{-\alpha k}\ll \epsilon ^{dM+2}$ and $\epsilon ^{-(l+({\dim G})/{2d})-M}e^{-{k}/{m}}\ll \epsilon ^{dM+2}$ . Combining this with Lemma 5.9, it follows that

Proof of Theorem 1.1

For fixed b, let $\eta _0=2(m+n)(1-({\dim _H \mathbf {Bad}^b(\epsilon )})/{mn})$ as in Proposition 5.4. It is enough to consider the case when $\mathbf {Bad}^b(\epsilon )$ is sufficiently close to the full dimension $mn$ , so we may assume $\dim _H\mathbf {Bad}^b(\epsilon )>\dim _H\mathbf {Bad}^0(\epsilon )$ and $\eta _0\leq 0.01$ . By Proposition 5.4, there is an a-invariant measure $\overline {\mu }\in \mathscr {P}(\overline {Y})$ such that $\operatorname {Supp}\overline {\mu }\subseteq \mathcal {L}_\epsilon \cup (\overline {Y}\setminus Y)$ , and $\pi _*\overline {\mu }(\overline {X}\setminus \mathfrak {S}_{\eta '})\leq \eta '$ for any $\eta _0\leq \eta '\leq 1$ . We also have a-invariant $\mu \in \mathscr {P}(Y)$ and $0\leq \widehat {\eta }\leq \eta _0$ such that

$$ \begin{align*}\overline{\mu}=(1-\widehat{\eta})\mu+\widehat{\eta}\delta_\infty.\end{align*} $$

In particular, for $\eta '=0.01$ , we have $\mu (\pi ^{-1}(\mathfrak {S}_{0.01}))\geq 0.99$ . We can choose $0<r<1$ such that $Y(r)\supset \pi ^{-1}(\mathfrak {S}_{0.01})$ . Note that the choice of r is independent of $\epsilon $ and b since $\mathfrak {S}_{0.01}$ is constructed in Proposition 5.3 independent to $\epsilon $ and b.

Let $\mathcal {A}^U$ be as in Proposition 2.8 for $\mu $ , $r_0$ , and $L=U$ , and let $\mathcal {A}^U_\infty $ be as in equation (2.12). It follows from item (3) of Proposition 5.4 that

$$ \begin{align*} {h_{\overline{\mu}}(a|\overline{\mathcal{A}^U_\infty})\ge(1-\widehat{\eta}^{{1}/{2}})\big(d-\tfrac{1}{2}\eta_0-d\widehat{\eta}^{{1}/{2}}\big).} \end{align*} $$

By the linearity of the entropy function with respect to the measure, we have

(5.17) $$ \begin{align} { h_\mu(a|\mathcal{A}^U_\infty)\ge(1+\widehat{\eta}^{{1}/{2}})^{-1}\big(d-\tfrac{1}{2}\eta_0-d\widehat{\eta}^{{1}/{2}}\big) \ge d-2d\widehat{\eta}^{{1}/{2}}-\tfrac{1}{2}\eta_0. } \end{align} $$

However, we shall get an upper bound of $h_{\mu }(a|\mathcal {A}^U_\infty )$ from Proposition 2.10 and Corollary 2.13. By Lemma 2.7, there exists $0<\delta <\min (({cr_0}/{16d_0})^2,r)$ such that $\mu (E_\delta )<0.01$ . Note that since $r_0$ depends only on G, the constants $C_1,C_2>0$ in Lemma 2.7 depend only on a and G, and hence $\delta $ is independent of $\epsilon $ even if the set $E_\delta $ depends on $\epsilon $ . We write $Z=Y(r)\setminus E_\delta $ for simplicity. Note that $\mu (Z)\ge \mu (Y(r))-\mu (E_\delta )>0.98$ .

By Proposition 2.8, $[y]_{\mathcal {A}^U}\subset B^U_{r_0}\cdot y$ for all $y\in Y$ , and $B_\delta ^U\cdot y\subset [y]_{\mathcal {A}^U}$ for all $y\in Z$ since $\delta <r$ . It follows from equation (3.1) that

(5.18) $$ \begin{align} {\text{ for all } y \in Y,\ [y]_{\mathcal{A}^U}\subset B^{U,d_\infty}_{C_0 r_0}\cdot y \quad\text{and} \quad \text{for all } y \in Z,\ B^{U,d_\infty}_{\delta/C_0}\cdot y\subset[y]_{\mathcal{A}^U},} \end{align} $$

where $B^{U,d_\infty }_r$ is the $d_\infty $ -ball of radius r around the identity in U. For simplicity, we may assume that $r_0 < {1}/{C_0}$ by choosing $r_0$ small enough.

Let M and $M'$ be the constants in Proposition 5.10, $r'=1-{1}/{2^{1/d}}$ , $R'=\epsilon ^{-M'}$ , $R=e^{{mn}/{d}}{C_0}/{\delta }R'$ , and $k=\lfloor ({mn\log R'})/{4d}\rfloor $ . Let $\mathcal {A}_1=a^{-j_1}\mathcal {A}^U$ and $\mathcal {A}_2=a^{j_2}\mathcal {A}^U$ , where

$$ \begin{align*} j_1=\bigg\lceil-\frac{mn}{d}\log r' \bigg\rceil \quad\text{and}\quad j_2=\bigg\lceil-\frac{mn}{d}\log\frac{\delta}{C_0 R'}\bigg\rceil. \end{align*} $$

By equation (5.18), we have that for any $y\in Y$ ,

(5.19) $$ \begin{align} { [y]_{\mathcal{A}_1}=a^{-j_1}[a^{j_1}y]_{\mathcal{A}^U} \subset a^{-j_1}B_{1}^{U,d_\infty}a^{j_1} \cdot y \subset B_{r'}^{U,d_\infty}\cdot y. } \end{align} $$

Similarly, it follows from equation (5.18) that $B^{U,d_\infty }_{R'}\cdot y\subset [y]_{\mathcal {A}_2}\subset B^{U,d_\infty }_R\cdot y$ for any $y\in a^{j_2}Z$ .

Let $\Omega =B^{U,d_\infty }_{r'}\operatorname {Supp}\mu $ . For any $v\in \mathbb {R}^d$ with $\|v\|\geq \epsilon ^{1/d}$ and $u \in B^{U,d_\infty }_{r'}$ ,

$$ \begin{align*}\|uv\| \geq \|v\| - \|(u-\mathrm{id})v\| \geq (1-r')\epsilon^{1/d} = (\epsilon/2)^{1/d},\end{align*} $$

and hence $\Omega \subseteq B^{U,d_\infty }_{r'} \mathcal {L}_\epsilon \subseteq \mathcal {L}_{{\epsilon }/{2}}$ . Since $\operatorname {Supp}\mu $ is an a-invariant set, we also have

$$ \begin{align*}a^{-3k}\Omega=(a^{-3k}B^{U,d_\infty}_{r'}a^{3k})a^{-3k}\operatorname{Supp}\mu\subseteq(a^{-3k}B^{U,d_\infty}_{r'}a^{3k}) \mathcal{L}_\epsilon\subseteq\mathcal{L}_{{\epsilon}/{2}}.\end{align*} $$

Applying Proposition 5.10 with $K=Y(r)$ , $\mathcal {A}=\mathcal {A}_2$ , and the same $R'$ , R, $\Omega $ as we just defined, for any $\epsilon>0$ and $y\in a^{4k}Y(r)\cap a^{j_2}Z$ ,

(5.20) $$ \begin{align} {1-\tau_y^{\mathcal{A}_2}(\Omega)\gg \epsilon^{dM+1}} \end{align} $$

since ${R'}/{R}$ is bounded below by a constant independent of $\epsilon $ .

By Proposition 2.10, we have

(5.21) $$ \begin{align} { (j_1+j_2)(d-h_\mu(a|\mathcal{A}^U_\infty)) = (j_1+j_2)(d-H_\mu(\mathcal{A}^U | a\mathcal{A}^U)) = (j_1+j_2)d-H_\mu (\mathcal{A}_1 | \mathcal{A}_2). } \end{align} $$

Note that the maximal entropy contribution of U for $a^{j_1+j_2}$ is $(j_1+j_2)d$ . Using equation (5.19), it follows from Corollary 2.13 with $\mathcal {A}=\mathcal {A}_1$ , $K=Y$ , and $B=B_{r'}^{U,d_\infty }$ that

(5.22) $$ \begin{align} { (j_1+j_2)d-H_\mu (\mathcal{A}_1 | \mathcal{A}_2) \geq - \int_Y \log\tau_y^{\mathcal{A}_2}(\Omega)\,d\mu(y). } \end{align} $$

Combining equations (5.20), (5.21), and (5.22), since $\mu (a^{4k}Y(r)\cap a^{j_2}Z)\geq \tfrac 12$ , we have

$$ \begin{align*} { (j_1+j_2)(d-h_\mu(a|\mathcal{A}^U_\infty)) \ge\int_{a^{4k}Y(r) \cap a^{j_2}Z}(1-\tau_y^{\mathcal{A}_2}(\Omega))\,d\mu(y) \gg \frac{1}{2}\epsilon^{dM+1}. } \end{align*} $$

It follows from equation (5.17) and $j_1+j_2\asymp \log (1/\epsilon )$ that

$$ \begin{align*} {\eta_0^{{1}/{2}}\gg 2d\widehat{\eta}^{{1}/{2}}+\tfrac{1}{2}\eta_0 \geq d-h_\mu(a|\mathcal{A}^U_\infty)\gg\epsilon^{dM+2}.} \end{align*} $$

Since $\eta _0=2(m+n)(1-({\dim _H \mathbf {Bad}^b(\epsilon )})/{mn})$ , we have

$$ \begin{align*}mn-\dim_H \mathbf{Bad}'(\epsilon)\ge c_0\epsilon^{2(dM+2)}\end{align*} $$

for some constant $c_0>0$ depending only on d.

6 Characterization of singular on average property and dimension estimates

In this section, we will show (2) ${\implies}$ (1) in Theorem 1.3. Let $A\in M_{m,n}$ and consider two subgroups

$$ \begin{align*} { G(A)\overset{\operatorname{def}}{=} A\mathbb{Z}^n + \mathbb{Z}^m \subset \mathbb{R}^m \quad\text{and}\quad G({^{t\!\!}A})\overset{\operatorname{def}}{=} {^{t\!\!}A}\mathbb{Z}^m + \mathbb{Z}^n \subset \mathbb{R}^n. } \end{align*} $$

If we view alternatively $G(A)$ as a subgroup of classes modulo $\mathbb {Z}^m$ , lying in the m-dimensional torus $\mathbb {T}^m$ , Kronecker’s theorem asserts that $G(A)$ is dense in $\mathbb {T}^m$ if and only if the group $G({^{t\!\!}A})$ has maximal rank $m+n$ over $\mathbb {Z}$ (see [Reference CasselsCas57, Ch. III, Theorem IV]). Thus, if $\text {rank}_{\mathbb {Z}} (G({^{t\!\!}A}))<m+n$ , then $\text {Bad}_A(\epsilon )$ has full Hausdorff dimension for any $\epsilon>0$ . Hence, throughout this section, we consider only matrices A for which $\text {rank}_{\mathbb {Z}} (G({^{t\!\!}A}))=m+n$ .

6.1 Best approximations

We set up a weighted version of the best approximations following [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20]. (See also [Reference Bugeaud, Kim, Lim and RamsBKLR21, Reference Bugeaud and LaurentBL05] and for the unweighted setting.)

Definition 6.1. Given $A\in M_{m,n}$ , we denote

$$ \begin{align*} M(\mathbf{y})= \inf_{\mathbf{q}\in\mathbb{Z}^n} \|{^{t\!\!}A}\mathbf{y}-\mathbf{q}\|_{\mathbf{s}}.\end{align*} $$

A sequence $(\mathbf {y}_i)_{i\geq 1}$ in $\mathbb {Z}^n$ is called a sequence of weighted best approximations to $^{t}\!A$ if the sequence satisfies the following properties:

  1. (1) setting $Y_i=\|\mathbf {y}_i\|_{\mathbf {r}}$ and $M_i=M(\mathbf {y}_i)$ , we have

    $$ \begin{align*} Y_1<Y_2<\cdots\quad \text{and}\quad M_1>M_2>\cdots{;} \end{align*} $$
  2. (2) $M(\mathbf {y})\geq M_i$ for all non-zero $\mathbf {y}\in \mathbb {Z}^m$ with $\|\mathbf {y}\|_{\mathbf {r}}<Y_{i+1}$ .

Our assumption $\text {rank}_{\mathbb {Z}} (G({^{t\!\!}A}))=m+n$ guarantees that $M(\mathbf {y})>0$ for all non-zero $\mathbf {y}\in \mathbb {Z}^m$ , and hence the existence of a sequence of best approximations to $^t A$ . Moreover, the following lemma says that $(Y_i)_{i\geq 1}$ has at least geometric growth.

Lemma 6.2. [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20, Proof of Lemma 4.3] There exists a positive integer V such that for all $i\geq 1,$

$$ \begin{align*} Y_{i+V}\geq 2Y_i.\end{align*} $$

In particular, there exist $c>0$ and $\gamma>1$ such that for all $i\geq 1$ , $Y_{i}\geq c\gamma ^i$ .

Remark 6.3. From the weighted Dirichlet’s theorem (see [Reference KleinbockKle98, Theorem 2.2]), one can check that $M_{k}Y_{k+1}\leq 1 $ for all $k\geq 1$ .

6.2 Characterization of singular on average property

In this section, we will characterize the singular on average property in terms of best approximations. At first, we will show A is singular on average if and only if $^{t}\!A$ is singular on average. To do this, following [Reference CasselsCas57, Ch. V], we prove a transference principle between two homogeneous approximations with weights. See also [Reference German and EvdokimovGE15, Reference GermanGer20].

Definition 6.4. Given positive numbers $\unicode{x3bb} _1,\ldots ,\unicode{x3bb} _d$ , consider the parallelepiped

$$ \begin{align*} { \mathcal{P}=\{ \mathbf{z}=(z_1,\ldots,z_d)\in \mathbb{R}^d:|z_i|\leq \unicode{x3bb}_i,\ i=1,\ldots,d \}. } \end{align*} $$

We call the parallelepiped

$$ \begin{align*} { \mathcal{P}^{*}=\bigg\{\mathbf{z}=(z_1,\ldots,z_d)\in \mathbb{R}^d:|z_i|\leq\frac{1}{\unicode{x3bb}_i}\prod_{j=1}^{d}\unicode{x3bb}_j,\ i=1,\ldots,d \bigg\} } \end{align*} $$

the pseudo-compound of $\mathcal {P}$ .

Theorem 6.5. [Reference German and EvdokimovGE15]

Let $\mathcal {P}$ be as in Definition 6.4 and let $\Lambda $ be a full-rank lattice in $\mathbb {R}^d$ . Then,

$$ \begin{align*} \mathcal{P}^{*}\cap \Lambda^{*} \neq \{\mathbf{0}\} \implies c\mathcal{P}\cap\Lambda \neq\{\mathbf{0}\}, \end{align*} $$

where $c=d^{{1}/{2(d-1)}}$ and $\Lambda ^{*}$ is the dual lattice of $\Lambda $ , that is, $\Lambda ^*=\{x\in \mathbb {R}^d : x\cdot y \in \mathbb {Z} \text { for all } y\in \Lambda \}.$

Corollary 6.6. For positive integer $m,n$ , let $d= m+n$ , and let $A\in M_{m,n}$ and $0<\epsilon <1$ be given. For all large enough $X\geq 1$ , if there exists a non-zero $\mathbf {q}\in \mathbb {Z}^n$ such that

(6.1) $$ \begin{align} { \langle A\mathbf{q}\rangle_{\mathbf{r}}\leq \epsilon T^{-1} \quad \text{and}\quad \|\mathbf{q}\|_{\mathbf{s}} \leq T, } \end{align} $$

then there exists a non-zero $\mathbf {y}\in \mathbb {Z}^m$ such that

$$ \begin{align*} { \langle^{t}\!A\mathbf{y}\rangle_{\mathbf{s}} \leq c^{({1}/{r_m}+{1}/{s_n})}\epsilon^{{r_m s_n}/({s_n +r_1 (1-s_n)})}T_1^{-1} \quad\text{and}\quad \|\mathbf{y}\|_{\mathbf{r}} \leq T_1, } \end{align*} $$

where c is as in Theorem 6.5 and $T_1=c^{{1}/{r_m}}\epsilon ^{-{r_m (1-s_n)}/({s_n+r_1 (1-s_n)})}T$ .

Proof. Consider the following two parallelepipeds:

$$ \begin{align*} \mathcal{Q}&=\left\{\mathbf{z}=(z_1,\ldots,z_d)\in \mathbb{R}^{d}: \begin{aligned} &|z_i| \leq \epsilon^{r_i}T^{-r_i}, \quad i=1,\ldots,m \\ &|z_{m+j}| \leq T^{s_j}, \quad j=1,\ldots,n \end{aligned} \right\},\\ \mathcal{P}&=\left\{\mathbf{z}=(z_1,\ldots,z_d)\in \mathbb{R}^{d}: \begin{aligned} &|z_i| \leq Z^{r_i}, \quad i=1,\ldots,m \\ &|z_{m+j}| \leq \delta^{s_j}Z^{-s_j}, \quad j=1,\ldots,n \end{aligned} \right\}, \end{align*} $$

where

$$ \begin{align*} { \delta=\epsilon^{{r_m s_n}/({s_n +r_1 (1-s_n)})}\quad\text{and}\quad Z=\epsilon^{-{r_m (1-s_n)}/({s_n +r_1 (1-s_n)})}T. } \end{align*} $$

Observe that the pseudo-compound of $\mathcal {P}$ is given by

$$ \begin{align*} \mathcal{P}^{*}=\left\{\mathbf{z}=(z_1,\ldots,z_d)\in \mathbb{R}^{d}: \begin{aligned} &|z_i| \leq \delta Z^{-r_i}, \quad i=1,\ldots,m \\ &|z_{m+j}| \leq \delta^{1-s_j}Z^{s_j}, \quad j=1,\ldots,n \end{aligned} \right\} \end{align*} $$

and that $\mathcal {Q} \subset \mathcal {P}^{*}$ since $\epsilon ^{r_i}T^{-r_i}\leq \delta Z^{-r_i}$ and $T^{s_j}\leq \delta ^{1-s_j}Z^{s_j}$ for all $i=1,\ldots ,m$ and $j=1,\ldots ,n$ .

Now, the existence of a non-zero solution $\mathbf {q}\in R_v^n$ of the inequalities in equation (6.1) implies that $(\begin {smallmatrix} I_m & A \\ & I_n \\ \end {smallmatrix}) \mathbb {Z}^d$ intersects $\mathcal {Q}$ , and thus $\mathcal {P}^{*}$ . By Theorem 6.5, $(\begin {smallmatrix} I_m & \\ -{^{t\!\!}A} & I_n \\ \end {smallmatrix}) \mathbb {Z}^d $ intersects $c\mathcal {P}$ , which concludes the proof of Corollary 6.6.

Corollary 6.7. Let $m,n$ be positive integers and $A\in M_{m,n}$ . Then, A is singular on average if and only if $\kern1pt{}^{t}\!A$ is singular on average.

Proof. It follows from Corollary 6.6.

Now, we will characterize the singular on average property in terms of best approximation. Let $A\in M_{m,n}$ be a matrix and $(\mathbf {y}_k)_{k\geq 1}$ be a sequence of weighted best approximations to $^{t}\!A$ and write

$$ \begin{align*}Y_k=\|\mathbf{y}_k\|_{\mathbf{r}},\quad M_k=\inf_{\mathbf{q}\in\mathbb{Z}^n} \|{^{t\!\!}A}\mathbf{y}_k-\mathbf{q}\|_{\mathbf{s}}. \end{align*} $$

Proposition 6.8. Let $A\in M_{m,n}$ be a matrix and let $(\mathbf {y}_k)_{k\geq 1}$ be a sequence of best approximations to $^{t}\!A$ . Then, the following are equivalent:

  1. (1) $^{t}\!A$ is singular on average;

  2. (2) for all $\epsilon>0$ ,

    $$ \begin{align*} {\lim\limits_{k\to\infty}\frac{1}{\log Y_{k}}|\{i\leq k:M_{i}Y_{i+1}>\epsilon\}|=0.} \end{align*} $$

Proof. ( $1)\ {\implies}\ (2)$ : Let $0<\epsilon <1$ . Observe that for each integer X with $Y_{k} \leq T < Y_{k+1}$ , the inequalities

(6.2) $$ \begin{align} { \|{^{t\!\!}A}\mathbf{p}-\mathbf{q}\|_{\mathbf{s}} \leq \epsilon T^{-1}\quad \text{and}\quad 0 < \|\mathbf{p}\|_{\mathbf{r}} \leq T } \end{align} $$

have a solution if and only if $T\leq ({\epsilon }/{M_k})$ . Thus, for each integer $\ell \in [\log _2{Y_k},\log _2{Y_{k+1}})$ the inequalities in equation (6.2) have no solutions for $T=2^\ell $ if and only if

(6.3) $$ \begin{align} { \log_2{\epsilon}-\log_2{M_k}<\ell<\log_2{Y_{k+1}}. } \end{align} $$

Now we assume that $^{t}\!A$ is singular on average. For given $\delta>0$ , if the set $\{k\in \mathbb {N}:M_k Y_{k+1}>\delta \}$ is finite, then it is done. Suppose the set $\{k\in \mathbb {N}:M_k Y_{k+1}>\delta \}$ is infinite and let

$$ \begin{align*} {\{k\in\mathbb{N}:M_{k}Y_{k+1}>\delta \}=\{j(1)<j(2)<\cdots<j(k)<\cdots:k\in\mathbb{N}\}.} \end{align*} $$

Set $\epsilon {\kern-1pt}={\kern-1pt}\delta /2$ and fix a positive integer V in Lemma 6.2. For an integer $\ell $ in $[\log _2{\kern-1pt} {Y_{j(k)+1}}{\kern-1pt}-{\kern-1pt}1, \log _2{Y_{j(k)+1}})$ , observe that

$$ \begin{align*} \log_2{\epsilon}-\log_2{M_{j(k)}} < \log_2{Y_{j(k)+1}}-1. \end{align*} $$

Hence, the inequalities in equation (6.2) have no solutions for $T=2^\ell $ by equation (6.3). By Lemma 6.2, $\log _2{Y_{j(k)+1+V}}-1\geq \log _2{Y_{j(k)+1}}$ . So, we have $\log _2{Y_{j(k+V)+1}}-1\geq \log _2{Y_{j(k)+1}}$ . Now fix $i=0,\ldots ,V-1$ . Then, the intervals

$$ \begin{align*}[\log_2{Y_{j(i+sV)+1}}-1,\log_2{Y_{j(i+sV)+1}}), \quad s=1,\ldots,k \end{align*} $$

are disjoint. Thus, for an integer $N\in [\log _2{Y_{j(i+kV)+1}},\log _2{Y_{j(i+(k+1)V)+1}})$ , the number of $\ell $ in $\{1,\ldots ,N\}$ such that equation (6.2) has no solutions for $T=2^\ell $ is at least k. Since $^{t}\!A$ is singular on average,

$$ \begin{align*} \frac{k}{\log_{2}{Y_{j(i+(k+1)V)+1}}}\leq\frac{1}{N}|\{\ell\in\{1,\ldots,N\}: \text{equation~}(6.2)\text{ has no solutions for }T=2^\ell \}| \end{align*} $$

tends to $0$ with k, which gives $({i+1+kV})/{\log _{2}{Y_{j(i+1+kV)}}}$ tends to $0$ with k for all $i=0,\ldots ,V-1$ . Thus, we have ${k}/{\log _{2}{Y_{j(k)}}}$ tends to $0$ with k.

For any $k\geq 1$ , there is an unique positive integer $s_k$ such that

$$ \begin{align*}j(s_k)\leq k < j(s_k +1),\end{align*} $$

and observe that $s_k=|\{i\leq k : M_i Y_{i+1}>\delta \}|$ . Thus, by the monotonicity of $Y_k$ , we have

$$ \begin{align*} { \lim_{k\to\infty}\frac{1}{\log_{2}Y_{k}}|\{i\leq k:M_{i}Y_{i+1}>\delta\}|\leq \lim_{k\to\infty}\frac{s_k}{\log_{2} Y_{j(s_k)}}=0. } \end{align*} $$

( $2)\ {\implies}\ (1)$ : Given $0<\epsilon <1$ , the number of integers $\ell $ in $[\log _2{Y_k},\log _2{Y_{k+1}})$ such that equation (6.2) has no solutions for $T=2^\ell $ is at most

$$ \begin{align*} \lceil \log_2{M_{k}Y_{k+1}}-\log_2{\epsilon} \rceil \leq \log_2{M_{k}Y_{k+1}}-\log_2{\epsilon}+1. \end{align*} $$

Thus, for an integer N in $[\log _2{Y_k},\log _2{Y_{k+1}})$ , we have

$$ \begin{align*} &\frac{1}{N} |\{\ell\in\{1,\ldots,N\}: \text{equation~}(6.2)\ \text{has no solutions for } T=2^\ell\}| \\ &\quad\leq\frac{1}{N}\sum_{i=1}^{k}\max(0,\log_2{M_{i}Y_{i+1}}-\log_2{\epsilon}+1)\\ &\quad\leq\frac{1}{\log_2{Y_k}}\sum_{i=1}^{k}\max(0,\log_2{M_{i}Y_{i+1}}-\log_2{\epsilon}+1). \end{align*} $$

Since $M_{i}Y_{i+1}\leq 1$ for each $i\geq 1$ ,

$$ \begin{align*} &\frac{1}{\log_2{Y_k}}\sum_{i=1}^{k}\max(0,\log_2{M_{i}Y_{i+1}}-\log_2{\epsilon}+1)\\ &\quad\leq\frac{1}{\log_2{Y_k}}(-\log_2{\epsilon}+1)|\{i\leq k : M_i Y_{i+1}>\epsilon/2\}|. \end{align*} $$

Therefore, $^{t}\!A$ is singular on average.

6.3 Modified Bugeaud–Laurent sequence

In this subsection, we construct the following modified Bugeaud–Laurent sequence assuming the singular on average property. We refer the reader to [Reference Bugeaud and LaurentBL05, §5] for the original version of the Bugeaud–Laurent sequence.

Proposition 6.9. Let $A\in M_{m,n}$ be such that $^{t}\!A$ is singular on average and let $(\mathbf {y}_k)_{k\geq 1}$ be a sequence of weighted best approximations to $^{t}\!A$ . For all S and R with $S>R>1$ , there exists an increasing function $\varphi :\mathbb {Z}_{\geq 1}\to \mathbb {Z}_{\geq 1}$ satisfying the following properties:

  1. (1) for any integer $i\geq 1$ ,

    (6.4) $$ \begin{align} { Y_{\varphi(i+1)}\geq RY_{\varphi(i)}\quad \text{and} \quad M_{\varphi(i)}Y_{\varphi(i+1)}\leq R; } \end{align} $$
  2. (2)

    (6.5) $$ \begin{align} { \limsup_{k\to\infty}\frac{k}{\log{Y_{\varphi(k)}}}\leq\frac{1}{\log{S}}. } \end{align} $$

Proof. The function $\varphi $ is constructed in the following way. Fix a positive integer V in Lemma 6.2 and let $\mathcal {J}=\{j\in \mathbb {Z}_{\geq 1}:M_j Y_{j+1}\leq R/S^3\}$ . Since $^{t}\!A$ is singular on average, by Proposition 6.8 with $\epsilon =R/S^3$ , we have

(6.6) $$ \begin{align} {\lim_{k\to\infty}\frac{1}{\log Y_{k}}|\{i\leq k:i\in\mathcal{J}^{c}\}|=0.} \end{align} $$

If the set $\mathcal {J}$ is finite, then we have $\lim \nolimits _{k\to \infty }Y_{k}^{1/k}=\infty $ by equation (6.6), and hence the proof of [Reference Bugeaud, Kim, Lim and RamsBKLR21, Theorem 2.2] implies that there exists a function $\varphi :\mathbb {Z}_{\geq 1}\to \mathbb {Z}_{\geq 1}$ for which

$$ \begin{align*} Y_{\varphi(i+1)}\geq RY_{\varphi(i)}\quad\text{and}\quad Y_{\varphi(i)+1}\geq R^{-1}Y_{\varphi(i+1)}. \end{align*} $$

The fact that $M_{i}Y_{i+1}\leq 1$ for all $i\geq 1$ implies $M_{\varphi (i)}Y_{\varphi (i+1)}\leq R$ . Equation (6.5) follows from $\lim \nolimits _{k\to \infty }Y_{k}^{1/k}=\infty $ , which concludes the proof of Proposition 6.9.

Now, suppose that $\mathcal {J}$ is infinite. Then there are two possible cases:

  1. (i) $\mathcal {J}$ contains all sufficiently large positive integers;

  2. (ii) there are infinitely many positive integers in $\mathcal {J}^{c}$ .

Case (i). Assume the first case and let $\psi (1)=\min \{j:\mathcal {J}\supset \mathbb {Z}_{\geq j}\}$ . Define the auxiliary increasing sequence $(\psi (i))_{i\geq 1}$ by

$$ \begin{align*} { \psi(i+1)=\min\{j\in\mathbb{Z}_{\geq 1}:SY_{\psi(i)}\leq Y_j\}, } \end{align*} $$

which is well defined since $(Y_i)_{i\geq 1}$ is increasing. Note that $\psi (i+1) \leq \psi (i)+\lceil \log _2{S}\rceil V$ since $Y_{\psi (i)+\lceil \log _2{S}\rceil V}\geq SY_{\psi (i)}$ by Lemma 6.2. Let us now define the sequence $(\varphi (i))_{i\geq 1}$ by, for each $i\geq 1$ ,

$$ \begin{align*} \varphi(i) = \begin{cases} \psi(i) & \text{if } M_{\psi(i)}Y_{\psi(i+1)} \leq R/S,\\ \psi(i+1)-1 & \text{otherwise}. \end{cases} \end{align*} $$

Then, the sequence $(\varphi (i))_{i\geq 1}$ is increasing and $\varphi \geq \psi $ .

Now we claim that for each $i \geq 1$ ,

(6.7) $$ \begin{align} { Y_{\varphi(i+1)}\geq SY_{\varphi(i)}> R Y_{\varphi(i)}\quad\text{and}\quad M_{\varphi(i)}Y_{\varphi(i+1)}\leq R, } \end{align} $$

which implies equation (6.5) since $Y_{\varphi (k)}\geq S^{k-1}Y_{\varphi (1)}$ for all $k\geq 1$ . Thus, the claim concludes the proof of Proposition 6.9.

Proof of equation (6.7)

There are four possible cases on the values of $\varphi (i)$ and $\varphi (i+1)$ .

$\bullet $ Assume that $\varphi (i)=\psi (i)$ and $\varphi (i+1) =\psi (i+1)$ . By the definition of $\psi (i+1)$ , we have

$$ \begin{align*} Y_{\varphi(i+1)}=Y_{\psi(i+1)}\geq SY_{\psi(i)}=S Y_{\varphi(i)}. \end{align*} $$

If $\psi (i)\neq \psi (i+1)-1$ , then by the definition of $\varphi (i)$ , we have

$$ \begin{align*} M_{\varphi(i)} Y_{\varphi(i+1)}=M_{\psi(i)} Y_{\psi(i+1)}\leq R/S \leq R. \end{align*} $$

If $\psi (i)= \psi (i+1)-1$ , then $\varphi (i+1)= \varphi (i)+1$ , and hence

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}=M_{\varphi(i)}Y_{\varphi(i)+1}\leq 1 \leq R. \end{align*} $$

This proves equation (6.7).

$\bullet $ Assume that $\varphi (i){\kern-1pt}={\kern-1pt}\psi (i)$ and $\varphi (i+1){\kern-1pt}={\kern-1pt} \psi (i{\kern-1pt}+{\kern-1pt}2){\kern-1pt}-{\kern-1pt}1$ . By the definition of $\psi (i+1)$ , we have

$$ \begin{align*} Y_{\varphi(i+1)}=Y_{\psi(i+2)-1}\geq Y_{\psi(i+1)}\geq SY_{\psi(i)}= S Y_{\varphi(i)}. \end{align*} $$

It follows from the minimality of $\psi (i+2)$ that $SY_{\psi (i+1)}> Y_{\psi (i+2)-1}$ . If $\psi (i+1)>\psi (i)+1$ , then $M_{\psi (i)} Y_{\psi (i+1)}\leq R/S$ by the definition of $\varphi (i)$ . Hence, we have

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}= M_{\psi(i)}Y_{\psi(i+2)-1}\leq SM_{\psi(i)}Y_{\psi(i+1)}\leq R. \end{align*} $$

If $\psi (i+1)=\psi (i)+1$ , then $M_{\psi (i)} Y_{\psi (i)+1} \leq R/S^3$ since $\psi (i)\in \mathcal {J}$ . Hence,

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}= M_{\psi(i)} Y_{\psi(i+2)-1}\leq SM_{\psi(i)} Y_{\psi(i)+1} \leq R/S^2 \leq R. \end{align*} $$

This proves equation (6.7).

$\bullet $ Assume that $\varphi (i){\kern-1pt}={\kern-1pt}\psi (i{\kern-1pt}+{\kern-1pt}1){\kern-1pt}-{\kern-1pt}1$ and $\varphi (i{\kern-1pt}+{\kern-1pt}1)= \psi (i{\kern-1pt}+{\kern-1pt}1)$ . Since $\psi (i{\kern-1pt}+{\kern-1pt}1)-1\in \mathcal {J}$ , we have

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}=M_{\psi(i+1)-1}Y_{\psi(i+1)}\leq R/S^3 \leq R. \end{align*} $$

If $\psi (i+1)-1=\psi (i)$ , then by the definition of $\psi (i+1)$ , we have

$$ \begin{align*} \frac{Y_{\varphi(i+1)}}{Y_{\varphi(i)}}=\frac{Y_{\psi(i+1)}}{Y_{\psi(i+1)-1}} =\frac{Y_{\psi(i+1)}}{Y_{\psi(i)}}\geq S. \end{align*} $$

If $\psi (i+1)-1>\psi (i)$ , then we have $M_{\psi (i)}Y_{\psi (i+1)}>R/S$ by the definition of $\varphi (i)$ , and we have $Y_{\psi (i+1)-1} < SY_{\psi (i)} \leq SY_{\psi (i)+1}$ from the minimality of $\psi (i+1)$ . We also have $M_{\psi (i)}Y_{\psi (i)+1} \leq R/S^3$ since $\psi (i)\in \mathcal {J}$ . Therefore,

$$ \begin{align*} \frac{Y_{\varphi(i+1)}}{Y_{\varphi(i)}} = \frac{Y_{\psi(i+1)}}{Y_{\psi(i+1)-1}}= \frac{M_{\psi(i)}Y_{\psi(i+1)}}{M_{\psi(i)}Y_{\psi(i+1)-1}} \geq\frac{R/S}{SM_{\psi(i)}Y_{\psi(i)+1}} \geq\frac{R/S}{R/S^2}=S. \end{align*} $$

This proves equation (6.7).

$\bullet $ Assume that $\varphi (i)=\psi (i+1)-1$ and $\varphi (i+1)=\psi (i+2)-1$ . As in the previous case, we have

$$ \begin{align*} \frac{Y_{\varphi(i+1)}}{Y_{\varphi(i)}} =\frac{Y_{\psi(i+2)-1}}{Y_{\psi(i+1)-1}} \geq\frac{Y_{\psi(i+1)}}{Y_{\psi(i+1)-1}}\geq S. \end{align*} $$

We have $SY_{\psi (i+1)}> Y_{\psi (i+2)-1}$ from the minimality of $\psi (i+2)$ . Thus, since $\psi (i+1)$ $-1\in \mathcal {J}$ , we have

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}=M_{\psi(i+1)-1}Y_{\psi(i+2)-1}=M_{\psi(i+1)-1}Y_{\psi(i+1)} \bigg(\frac{Y_{\psi(i+2)-1}}{Y_{\psi(i+1)}}\bigg) \leq R. \end{align*} $$

This proves equation (6.7).

Case (ii). Now we assume the second case and let $j_0 =\min \mathcal {J}$ . Partition $\mathbb {Z}_{\geq j_0}$ into disjoint subset

$$ \begin{align*} \mathbb{Z}_{\geq j_0}= C_1 \sqcup D_1 \sqcup C_2 \sqcup D_2 \sqcup \cdots, \end{align*} $$

where $C_i\subset \mathcal {J}$ and $D_j\subset \mathcal {J}^{c}$ are sets of consecutive integers with

$$ \begin{align*} \max C_i < \min D_i \leq \max D_i < \min C_{i+1} \end{align*} $$

for all $i\geq 1$ . We consider the following two subcases.

Case (ii)-1. If there is $i_0 \geq 1$ such that $|C_i|< 3\lceil \log _2{S}\rceil V$ for all $i\geq i_0$ , then we have, for $k_0 = \min C_{i_0}$ ,

$$ \begin{align*} { \frac{k}{\log Y_k}\leq \frac{k_0+(3\lceil\log_2{S}\rceil V +1)|\{i\leq k: i\in\mathcal{J}^{c}\}|}{\log Y_{k}}, } \end{align*} $$

since there exists an element of $\mathcal {J}^c$ in any finite sequence of $3\lceil \log _2{S}\rceil V +1$ consecutive integers at least $k_0$ . Therefore, $\lim \limits _{k\to \infty }Y_{k}^{1/k}=\infty $ by equation (6.6) and this concludes the proof of Proposition 6.9 following the proof when $\mathcal {J}$ is finite at the beginning.

Case (ii)-2. The remaining case is that the set

$$ \begin{align*} { \{i:|C_{i}|\geq 3\lceil\log_2{S}\rceil V \}=\{i(1)<i(2)<\cdots<i(k)<\cdots:k\in\mathbb{N}\} } \end{align*} $$

is infinite.

For each $k\geq 1$ , let us define an increasing finite sequence $(\psi _k (i))_{1\leq i \leq m_k +1}$ of positive integers by setting $\psi _k (1)=\min C_{i(k)}$ and by induction,

$$ \begin{align*} \psi_k (i+1) = \min \{j\in C_{i(k)}: SY_{\psi_k (i)} \leq Y_j \}, \end{align*} $$

as long as this set is non-empty. Since $C_{i(k)}$ is a finite sequence of consecutive positive integers with length at least $3\lceil \log _2{S}\rceil V$ and $Y_{i+\lceil \log _2{S}\rceil V}\geq SY_{i}$ for every $i\geq 1$ by Lemma 6.2, there exists an integer $m_k \geq 2$ such that $\psi _k (i)$ is defined for $i=1,\ldots ,m_k +1$ . Note that $\psi _k (i)$ belongs to $\mathcal {J}$ since $C_{i(k)}\subset \mathcal {J}$ .

As in Case (i), let us define an increasing finite sequence $(\varphi _k (i))_{1\leq i\leq m_k}$ of positive integers by

$$ \begin{align*} \varphi_k (i) =\begin{cases} \psi_k (i) & \text{if } M_{\psi_k (i)}Y_{\psi_k (i+1)} \leq R/S,\\ \psi_k (i+1)-1 & \text{otherwise}. \end{cases} \end{align*} $$

Following the proof of Case (i), we have for each $i=1,\ldots ,m_k -1$ ,

(6.8) $$ \begin{align} { Y_{\varphi_k (i+1)}\geq S Y_{\varphi_k (i)} \quad\text{and}\quad M_{\varphi_k (i)}Y_{\varphi_k (i+1)} \leq R. } \end{align} $$

Note that $\varphi _k (m_k)<\varphi _{k+1}(1)$ . Let us define an increasing finite sequence $(\varphi _k'(i))_{1\leq i\leq n_k +1}$ of positive integers to interpolate between $\varphi _k (m_k)$ and $\varphi _{k+1}(1)$ . Let $j_0 =\varphi _{k+1}(1)$ . If the set $\{j\in \mathbb {Z}_{\geq \varphi _k (m_k)}: Y_{j_0}\geq RY_j\}$ is empty, then we set $n_k =0$ and $\varphi _k'(1)=j_0 = \varphi _{k+1}(1)$ . Otherwise, following [Reference Bugeaud, Kim, Lim and RamsBKLR21, Theorem 2.2], by decreasing induction, let $n_k \in \mathbb {Z}_{\geq 1}$ be the maximal positive integer such that there exists $j_1,\ldots , j_{n_k}\in \mathbb {Z}_{\geq 1}$ such that for $\ell =1,\ldots ,n_k$ , the set $\{j\in \mathbb {Z}_{\geq \varphi _k (m_k)}: Y_{j_{\ell -1}}\geq RY_j\}$ is non-empty and for $\ell =1,\ldots , n_k +1$ , the integer $j_\ell $ is its largest element. Set $\varphi _k'(i)=j_{n_k +1 -i}$ for $i=1,\ldots , n_k +1$ . Then, the sequence $(\varphi _k'(i))_{1\leq i\leq n_k +1}$ is contained in $[\varphi _k(m_k),\varphi _{k+1}(1)]$ and satisfies that for $i=1,\ldots ,n_k$ ,

(6.9) $$ \begin{align} { Y_{\varphi_k' (i+1)}\geq RY_{\varphi_k'(i)} \quad\text{and}\quad M_{\varphi_k' (i)}Y_{\varphi_k'(i+1)}\leq R } \end{align} $$

from the proof of [Reference Bugeaud, Kim, Lim and RamsBKLR21, Theorem 2.2].

Now, putting alternatively together the sequences $(\varphi _k (i))_{1\leq i\leq m_k -1}$ and $(\varphi _k' (i))_{1\leq i\leq r_k}$ as k ranges over $\mathbb {Z}_{\geq 1}$ , we define $N_k = \sum _{\ell =1}^{k-1}(m_\ell -1 +n_\ell )$ and

$$ \begin{align*} \varphi (i) =\begin{cases} \varphi_k (i-N_k) & \text{if } 1+N_k \leq i\leq m_k -1 +N_k,\\ \varphi_k' (i+1-m_k -N_k) &\text{if } m_k +N_k \leq i\leq r_k -1 +m_k +N_k. \end{cases} \end{align*} $$

Here, we use the standard convention that an empty sum is zero. With equation (6.8) for $i=1,\ldots ,m_k -2$ and equation (6.9) for $i=1,\ldots ,n_k$ , since $\varphi _k'(n_k +1) = \varphi _{k+1}(1)$ , it is enough to show the following lemma to prove that the map $\varphi $ satisfies equation (6.4).

Lemma 6.10. For every $k\in \mathbb {Z}_{\geq 1}$ , we have

(6.10) $$ \begin{align} { Y_{\varphi_k'(1)}\geq RY_{\varphi_{k}(m_k -1)}\quad\text{and}\quad M_{\varphi_k(m_k-1)}Y_{\varphi_k'(1)}\leq R. } \end{align} $$

Proof. Since $\varphi _k'(1)\geq \varphi _k(m_k)$ and equation (6.8) with $i=m_k-1$ , we have

$$ \begin{align*} Y_{\varphi_k'(1)}\geq Y_{\varphi_k(m_k)} \geq SY_{\varphi_k(m_k-1)} \geq RY_{\varphi_{k}(m_k -1)}, \end{align*} $$

which proves the left-hand side of equation (6.10). If $\varphi _k'(1)=\varphi _k(m_k)$ , then equation (6.8) with $i=m_k-1$ gives the right-hand side of equation (6.10).

Now assume that $\varphi _k'(1)>\varphi _k(m_k)$ . By the maximality of $n_k$ , we have $Y_{\varphi _k'(1)}\leq RY_{\varphi _k(m_k)}$ . First, we will prove that $\varphi _k(m_k)=\psi _k (m_k)$ . For a contradiction, assume that $\varphi _k(m_k)=\psi _k(m_k+1)-1>\phi _k(m_k)$ . Following the third subcase of the proof of equation (6.7), we have

$$ \begin{align*} \frac{Y_{\psi_k (m_k+1)}}{Y_{\psi_k(m_k+1)-1}}=\frac{M_{\psi_k(m_k)}Y_{\psi_k (m_k+1)}}{M_{\psi_k(m_k)}Y_{\psi_k(m_k+1)-1}}\geq S. \end{align*} $$

Hence, by the construction of $\varphi _k'(1)$ , we have $\varphi _k'(1)=\varphi _k(m_k)$ , which is a contradiction to our assumption $\varphi _k'(1)>\varphi _k(m_k)$ .

To show the right-hand side of equation (6.10), we consider two possible values of $\varphi _k(m_k-1)$ .

Assume that $\varphi _k(m_k-1)=\psi _k(m_k-1)$ . If $\psi _k(m_k-1)>\psi _k(m_k)-1$ , then by the definition of $\varphi _k(m_k{\kern-1pt}-{\kern-1pt}1)$ , we have $M_{\psi _k(m_k-1)}Y_{\psi _k(m_k)}{\kern-1pt}\leq{\kern-1pt} R/S$ . If $\psi _k(m_k{\kern-1pt}-{\kern-1pt}1){\kern-1pt}={\kern-1pt}\psi _k(m_k){\kern-1pt}-{\kern-1pt}1$ , then $M_{\psi _k(m_k-1)}Y_{\psi _k(m_k)}{\kern-1pt}\leq{\kern-1pt} R/S^3 \leq R/S$ , since $\psi _k(m_k){\kern-1pt}-{\kern-1pt}1{\kern-1pt}\in{\kern-1pt} \mathcal {J}$ . Since $\varphi _k(m_k)=\psi _k(m_k)$ , we have

$$ \begin{align*} M_{\varphi_k(m_k-1)}Y_{\varphi_k'(1)} = M_{\psi_k(m_k-1)}Y_{\psi_k(m_k)}\bigg(\frac{Y_{\varphi_k'(1)}}{Y_{\varphi_k(m_k)}}\bigg) \leq R, \end{align*} $$

which proves the right-hand side of equation (6.10).

Assume that $\varphi _k(m_k{\kern-1pt}-{\kern-1pt}1)=\psi _k(m_k){\kern-1pt}-{\kern-1pt}1$ . Since $\varphi _k(m_k){\kern-1pt}={\kern-1pt}\psi _k(m_k)$ and $\psi _k(m_k){\kern-1pt}-{\kern-1pt}1{\kern-1pt}\in{\kern-1pt} \mathcal {J}$ , we have

$$ \begin{align*} M_{\varphi_k(m_k-1)}Y_{\varphi_k'(1)} = M_{\psi_k(m_k)-1}Y_{\psi_k(m_k)}\bigg(\frac{Y_{\varphi_k'(1)}}{Y_{\varphi_k(m_k)}}\bigg) \leq R, \end{align*} $$

which proves the right-hand side of equation (6.10), and concludes the proof of Lemma 6.10.

Finally, we will show equation (6.5) for the map $\varphi $ . Since there exists an element of $\mathcal {J}^c$ in any finite sequence of $3\lceil \log _2{S}\rceil V +1$ consecutive integers in the complement of $\bigcup _{k\geq 1} C_{i(k)}$ , there exists $c_0 \geq 0$ such that for every $k\geq 1$ , we have

$$ \begin{align*} \frac{|\{j\leq \varphi(k): j\notin \bigcup_{k\geq 1} C_{i(k)} \}|}{\log Y_{\varphi(k)}} \leq \frac{c_0+(3\lceil\log_2{S}\rceil V +1)|\{j\leq \varphi(k): j \in\mathcal{J}^{c}\}|}{\log Y_{\varphi(k)}}, \end{align*} $$

which converges to $0$ as $k\to +\infty $ by equation (6.6). Let us define

$$ \begin{align*}n(k)= |\{i\leq k : Y_{\varphi(i)} \geq SY_{\varphi(i+1)}\}|.\end{align*} $$

For each integer $\ell \geq 1$ , since $Y_{i+\lceil \log _2{S}\rceil V}\geq SY_{i}$ for every $i\geq 1$ by Lemma 6.2, and by the maximality of $m_\ell $ in the construction of $(\varphi _\ell (i))_{1\leq i\leq m_\ell }$ , we have $|\{j\in C_{i(\ell )}: j\geq \varphi _{\ell }(m_\ell )\}|\leq 2\lceil \log _2{S}\rceil V$ . If $\varphi (i)$ belongs to $C_{i(\ell )}$ but $\varphi (i+1)$ does not, then $\varphi (i)\geq \varphi _\ell (m_\ell )$ . If $\varphi (i)$ and $\varphi (i+1)$ belong to $C_i(\ell )$ , then $\varphi $ and $\varphi _\ell $ coincide on i and $i+1$ . Thus, by equation (6.8), we have

$$ \begin{align*} k-n(k)&=|\{i\leq k : Y_{\varphi(i)} < SY_{\varphi(i+1)}\}|\\ &\leq (2\lceil\log_2{S}\rceil V) \bigg|\bigg\{j\leq \varphi(k): j\notin \bigcup_{k\geq 1} C_{i(k)}\bigg\}\bigg|. \end{align*} $$

Therefore, we have

$$ \begin{align*} \limsup_{k\to\infty}\frac{k}{\log Y_{\varphi(k)}}&=\limsup_{k\to\infty}\frac{n(k)+k-n(k)}{\log Y_{\varphi(k)}} = \limsup \frac{n(k)}{\log Y_{\varphi(k)}}\\ &\leq \limsup_{k\to\infty}\frac{n(k)}{\log S^{n(k)-1}Y_{\varphi(1)}}=\frac{1}{\log S}. \end{align*} $$

This proves equation (6.5) and concludes the proof of Proposition 6.9.

6.4 Dimension estimates

Following the notation in [Reference Bugeaud, Harrap, Kristensen and VelaniBHKV10], given a sequence $\{\mathbf {y}_i\}$ in $\mathbb {Z}^m\setminus \{\mathbf {0}\}$ and $\alpha \in (0,1/2)$ , let

$$ \begin{align*} \text{Bad}_{\{\mathbf{y}_i\}}^\alpha \overset{\operatorname{def}}{=}\{\mathbf{\theta}\in\mathbb{R}^m:|\mathbf{\theta}\cdot\mathbf{y}_{i}|_{\mathbb{Z}}\geq\alpha\ \text{for all}\ i\geq1\}. \end{align*} $$

Proposition 6.11. [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20]

Let $A\in M_{m,n}$ be a matrix and let $(\mathbf {y}_k)_{k\geq 1}$ be a sequence of weighted best approximations to $^{t}\!A$ , and let $R>1$ and $\alpha \in (0,1/2)$ be given. Suppose that there exists an increasing function $\varphi :\mathbb {Z}_{\geq 1}\to \mathbb {Z}_{\geq 1}$ such that for any integer $i\geq 1$ ,

$$ \begin{align*} M_{\varphi(i)}Y_{\varphi(i+1)}\leq R. \end{align*} $$

Then, $\textrm {Bad}_{\{\mathbf {y}_{\varphi (i)}\}}^\alpha $ is a subset of $\textrm {Bad}_A(\epsilon )$ , where $\epsilon =({1}/{R})({\alpha ^2}/{4mn})^{1/\delta }$ and $\delta =\min \{r_i,s_j:1\leq i\leq m, 1\leq j\leq n\}$ .

Proof. In the proof of [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20, Theorem 1.11], the condition $Y_{\varphi (i)+1}\geq R^{-1}Y_{\varphi (i+1)}$ is used. However, the assumption $M_{\varphi (i)}Y_{\varphi (i+1)}\leq R$ also implies the same conclusion.

Proposition 6.12. [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20]

For any $\alpha \in (0,1/2)$ , there exists $R(\alpha )>1$ with the following property. Let $(\mathbf {y}_k)_{k\geq 1}$ be a sequence in $\mathbb {Z}^m\setminus \{\mathbf {0}\}$ such that $\|\mathbf {y}_{k+1}\|_{\mathbf {r}}/\|\mathbf {y}_{k}\|_{\mathbf {r}}\geq R(\alpha )$ for all $k\geq 1$ . Then,

$$ \begin{align*} \textrm{dim}_{H}(\textrm{Bad}_{\{\mathbf{y}_i\}}^\alpha) \geq m-C\limsup_{k\to\infty}\frac{k}{\log{\|\mathbf{y}_{k}\|_{\mathbf{r}}}} \end{align*} $$

for some positive constant $C=C(\alpha )$ .

Proof. The proof of [Reference Chow, Ghosh, Guan, Marnet and SimmonsCGGMS20, Theorem 6.1] concludes this proposition.

The two propositions are used in [Reference Bugeaud, Kim, Lim and RamsBKLR21, Theorem 5.1] in the unweighted setting.

Proof of Theorem 1.3 (2) ${\implies} $ (1)

Suppose A is singular on average. By Corollary 6.7, $^{t}\!A$ is also singular on average. Let $(\mathbf {y}_k)_{k\geq 1}$ be a sequence of weighted best approximations to $^{t}\!A$ . Then, by Propositions 6.9, 6.11, and 6.12, for each $S>R(\alpha )>1$ , we have

$$ \begin{align*} \textrm{dim}_{H}(\textrm{Bad}_A(\epsilon)) &\geq \textrm{dim}_{H}(\textrm{Bad}_{\{\mathbf{y}_{\varphi(i)}\}}^\alpha)\\ &\geq m- C\limsup_{k\to\infty}\frac{k}{\log{Y_{\varphi(k)}}}\\ &\geq m- \frac{C}{\log{S}}, \end{align*} $$

where $\epsilon =({1}/{R)(\alpha )}({\alpha ^2}/{4mn})^{1/\delta }$ . Taking $S\to \infty $ , we have $\textrm {dim}_{H}(\textrm {Bad}_A(\epsilon ))=m$ for $\epsilon =({1}/{R(\alpha )})({\alpha ^2}/{4mn})^{1/\delta }$ .

Acknowledgements

We would like to thank Manfred Einsiedler and Frédéric Paulin for helpful discussions and valuable comments. S.L. is an associate member of KIAS. S.L. and T.K. were supported by the National Research Foundation of Korea under Project Number NRF-2020R1A2C1A01011543. T.K. was supported by the National Research Foundation of Korea under Project Number NRF-2021R1A6A3A13039948. W.K. was supported by the Korea Foundation for Advanced Studies.

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Figure 0

Figure 1 Intersection of $\Theta ^{+}\times \Theta ^{-}$ and $[y]_{a^{j_{1}}\mathcal {A}}$.