We present the first of two papers dedicated to verifying the Australian Epoch of Reionisation pipeline (AusEoRPipe) through simulation. The AusEoRPipe aims to disentangle 21-cm radiation emitted by gas surrounding the very first stars from contaminating foreground astrophysical sources and has been in the development for close to a decade. In this paper, we build an accurate 21-cm sky model that can be used by the WODEN simulation software to create visibilities containing a predictable 21-cm signal. We verify that the power spectrum (PS) estimator CHIPS can recover this signal in the absence of foregrounds. We also investigate how measurements in Fourier-space are correlated and how their gridded density affects the PS. We measure and fit for this effect using Gaussian-noise simulations of the Murchison Widefield Array (MWA) phase I layout. We find a gridding density correction factor of 2.651 appropriate for integrations equal to or greater than 30 minutes of data, which contain observations with multiple primary beam pointings and LSTs. Paper II of this series will use the results of this paper to test the AusEoRPipe in the presence of foregrounds and instrumental effects.

We present the pulse arrival times and high-precision dispersion measure estimates for 14 millisecond pulsars observed simultaneously in the 300 $-$ 500 MHz and 1260 $-$ 1460 MHz frequency bands using the upgraded Giant Metrewave Radio Telescope. The data spans over a baseline of 3.5 years (2018-2021), and is the first official data release made available by the Indian Pulsar Timing Array collaboration. This data release presents a unique opportunity for investigating the interstellar medium effects at low radio frequencies and their impact on the timing precision of pulsar timing array experiments. In addition to the dispersion measure time series and pulse arrival times obtained using both narrowband and wideband timing techniques, we also present the dispersion measure structure function analysis for selected pulsars. Our ongoing investigations regarding the frequency dependence of dispersion measures have been discussed. Based on the preliminary analysis for five millisecond pulsars, we do not find any conclusive evidence of chromaticity in dispersion measures. Data from regular simultaneous two-frequency observations are presented for the first time in this work. This distinctive feature leads us to the highest precision dispersion measure estimates obtained so far for a subset of our sample. Simultaneous multi-band upgraded Giant Metrewave Radio Telescope observations in 300 $-$ 500 MHz and 1260 $-$ 1460 MHz are crucial for high-precision dispersion measure estimation and for the prospect of expanding the overall frequency coverage upon the combination of data from the various Pulsar Timing Array consortia in the near future. Parts of the data presented in this work are expected to be incorporated into the upcoming third data release of the International Pulsar Timing Array.

Most applications of Bayesian Inference for parameter estimation and model selection in astrophysics involve the use of Monte Carlo techniques such as Markov Chain Monte Carlo (MCMC) and nested sampling. However, these techniques are time-consuming and their convergence to the posterior could be difficult to determine. In this study, we advocate variational inference as an alternative to solve the above problems, and demonstrate its usefulness for parameter estimation and model selection in astrophysics. Variational inference converts the inference problem into an optimisation problem by approximating the posterior from a known family of distributions and using Kullback–Leibler divergence to characterise the difference. It takes advantage of fast optimisation techniques, which make it ideal to deal with large datasets and makes it trivial to parallelise on a multicore platform. We also derive a new approximate evidence estimation based on variational posterior, and importance sampling technique called posterior-weighted importance sampling for the calculation of evidence, which is useful to perform Bayesian model selection. As a proof of principle, we apply variational inference to five different problems in astrophysics, where Monte Carlo techniques were previously used. These include assessment of significance of annual modulation in the COSINE-100 dark matter experiment, measuring exoplanet orbital parameters from radial velocity data, tests of periodicities in measurements of Newton’s constant G, assessing the significance of a turnover in the spectral lag data of GRB 160625B, and estimating the mass of a galaxy cluster using weak gravitational lensing. We find that variational inference is much faster than MCMC and nested sampling techniques for most of these problems while providing competitive results. All our analysis codes have been made publicly available.

We introduce pinta, a pipeline for reducing the upgraded Giant Metre-wave Radio Telescope (uGMRT) raw pulsar timing data, developed for the Indian Pulsar Timing Array experiment. We provide a detailed description of the workflow and usage of pinta, as well as its computational performance and RFI mitigation characteristics. We also discuss a novel and independent determination of the relative time offsets between the different back-end modes of uGMRT and the interpretation of the uGMRT observation frequency settings and their agreement with results obtained from engineering tests. Further, we demonstrate the capability of pinta to generate data products which can produce high-precision TOAs using PSR J1909 $-$ 3744 as an example. These results are crucial for performing precision pulsar timing with the uGMRT.

The past few decades have seen the burgeoning of wide-field, high-cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy; however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO) whose primary science objective is the optical follow-up of gravitational wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near-real time to fully exploit the time domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this ‘off-the-shelf’ pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to subpixel levels, and that measured L-band photometries are accurate to $\sim50$ mmag at $m_L\sim16$ and $\sim200$ mmag at $m_L\sim18$. These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, gotophoto, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic ‘obs package’ that others can build upon, should they wish to use the LSST Science Pipelines to process data from other facilities.

To make a power spectrum (PS) detection of the 21-cm signal from the Epoch of Reionisation (EoR), one must avoid/subtract bright foreground sources. Sources such as Fornax A present a modelling challenge due to spatial structures spanning from arc seconds up to a degree. We compare modelling with multi-scale (MS) CLEAN components to ‘shapelets’, an alternative set of basis functions. We introduce a new image-based shapelet modelling package, SHAMFI. We also introduce a new CUDA simulation code (WODEN) to generate point source, Gaussian, and shapelet components into visibilities. We test performance by modelling a simulation of Fornax A, peeling the model from simulated visibilities, and producing a residual PS. We find the shapelet method consistently subtracts large-angular-scale emission well, even when the angular resolution of the data is changed. We find that when increasing the angular resolution of the data, the MS CLEAN model worsens at large angular scales. When testing on real Murchison Widefield Array data, the expected improvement is not seen in real data because of the other dominating systematics still present. Through further simulation, we find the expected differences to be lower than obtainable through current processing pipelines. We conclude shapelets are worthwhile for subtracting extended galaxies, and may prove essential for an EoR detection in the future, once other systematics have been addressed.