Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-09T09:30:09.931Z Has data issue: false hasContentIssue false

Escape rates for special flows and their higher order asymptotics

Published online by Cambridge University Press:  25 September 2017

FABIAN DREHER
Affiliation:
Fachbereich 3 – Mathematik und Informatik, Universität Bremen, Bibliothekstraße 1, 28359 Bremen, Germany email [email protected], [email protected]
MARC KESSEBÖHMER
Affiliation:
Fachbereich 3 – Mathematik und Informatik, Universität Bremen, Bibliothekstraße 1, 28359 Bremen, Germany email [email protected], [email protected]

Abstract

In this paper escape rates and local escape rates for special flows are studied. In a general context the first result is that the escape rate depends monotonically on the ceiling function and fulfils certain scaling, invariance and continuity properties. In the context of metric spaces local escape rates are considered. If the base transformation is ergodic and exhibits an exponential convergence in probability of ergodic sums, then the local escape rate with respect to the flow is just the local escape rate with respect to the base transformation, divided by the integral of the ceiling function. Also, a reformulation with respect to induced pressure is presented. Finally, under additional regularity conditions higher order asymptotics for the local escape rate are established.

Type
Original Article
Copyright
© Cambridge University Press, 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ambrose, W.. Representation of ergodic flows. Ann. of Math. (2) 42(3) (1941), 723739.Google Scholar
Bandtlow, O. F., Jenkinson, O. and Pollicott, M.. Periodic points, escape rates and escape measures. Ergodic Theory, Open Dynamics, and Coherent Structures (Springer Proceedings in Mathematics & Statistics, 70) . Eds. Bahsoun, W., Bose, C. and Froyland, G.. Springer, New York, 2014, pp. 4158.Google Scholar
Bunimovich, L. A. and Yurchenko, A.. Where to place a hole to achieve a maximal escape rate. Israel J. Math. 182(1) (2011), 229252.Google Scholar
Cipriano, I.. Entry times, escape rates and smoothness of stationary measures. PhD Thesis, University of Warwick, 2015.Google Scholar
Cristadoro, G., Knight, G. and Degli Esposti, M.. Follow the fugitive: an application of the method of images to open systems. J. Phys. A 46(27) (2013), 272001.Google Scholar
Collet, P., Martnez, S. and Schmitt, B.. The Pianigiani–Yorke measure for topological Markov chains. Israel J. Math. 97(1) (1997), 6170.Google Scholar
Dreher, F.. Über Ausströmraten spezieller Flüsse. PhD Thesis, Universität Bremen, 2015.Google Scholar
Ellis, R. S.. Entropy, Large Deviation, and Statistical Mechanics (Grundlehren der mathematischen Wissenschaften, 271) . Springer, New York, 1985.Google Scholar
Ferguson, A. and Pollicott, M.. Escape rates for Gibbs measures. Ergod. Th. & Dynam. Sys. 32(3) (2012), 961988.Google Scholar
Gurevich, B. M.. Construction of increasing partitions for special flows. Theory Probab. Appl. 10(4) (1965), 627645.Google Scholar
Jacobs, K.. Neuere Methoden und Ergebnisse der Ergodentheorie (Ergebnisse der Mathematik und ihrer Grenzgebiete, 29) . Springer, Berlin, 1960.Google Scholar
Jaerisch, J., Kesseböhmer, M. and Lamei, S.. Induced topological pressure for countable state Markov shifts. Stoch. Dyn. 14(2) (2014), 1350016-1–31.Google Scholar
Keane, M.. Strongly mixing g-measures. Invent. Math. 16 (1972), 309324.Google Scholar
Kesseböhmer, M.. Large deviation for weak Gibbs measures and multifractal spectra. Nonlinearity 14(2) (2001), 395409.Google Scholar
Keller, G. and Liverani, C.. Rare events, escape rates and quasistationarity: some exact formulae. J. Stat. Phys. 135(3) (2009), 519534.Google Scholar
Katz, M. and Thomasian, A. J.. A bound for the law of large numbers for discrete Markov processes. Ann. Math. Statist. 32(1) (1961), 336337.Google Scholar
Ledrappier, F.. Principe variationnel et systèmes dynamiques symboliques. Z. Wahrscheinlichkeitsth. Verw. Geb. 30 (1974), 185202.Google Scholar
Lind, D. A.. Perturbations of shifts of finite type. SIAM J. Discrete Math. 2(3) (1989), 350365.Google Scholar
Liverani, C. and Maume-Deschamps, V.. Lasota–Yorke maps with holes: conditionally invariant probability measures and invariant probability measures on the survivor set. Ann. Inst. Henri Poincaré (B) Probab. Stat. 39(3) (2003), 385412.Google Scholar
Magnus, J. R. and Neudecker, H.. Matrix Differential Calculus with Applications in Statistics and Econometrics (Wiley Series in Probability and Mathematical Statistics) , Revised edn. John Wiley, Chichester, 1991.Google Scholar
Pianigiani, G. and Yorke, J. A.. Expanding maps on sets which are almost invariant. Decay and chaos. Trans. Amer. Math. Soc. 252 (1979), 351366.Google Scholar
Rockafellar, R. T.. Convex Analysis (Princeton Mathematical Series, 28) , Second printing edn. Princeton University Press, Princeton, NJ, 1972.Google Scholar
Rousseau, J.. Recurrence rates for observations of flows. Ergod. Th. & Dynam. Sys. 32(5) (2012), 17271751.Google Scholar
Walters, P.. An Introduction to Ergodic Theory (Graduate Texts in Mathematics, 79) , First softcover printing edn. Springer, New York, 2000.Google Scholar
Yuri, M.. Zeta functions for certain non-hyperbolic systems and topological Markov approximations. Ergod. Th. & Dynam. Sys. 18 (1998), 15891612.Google Scholar