matvis: A matrix-based visibility simulator for fast forward modelling of many-element 21 cm arrays
Abstract
Detection of the faint 21 cm line emission from the Cosmic Dawn and Epoch of Reionisation will require not only exquisite control over instrumental calibration and systematics to achieve the necessary dynamic range of observations but also validation of analysis techniques to demonstrate their statistical properties and signal loss characteristics. A key ingredient in achieving this is the ability to perform high-fidelity simulations of the kinds of data that are produced by the large, many-element, radio interferometric arrays that have been purpose-built for these studies. The large scale of these arrays presents a computational challenge, as one must simulate a detailed sky and instrumental model across many hundreds of frequency channels, thousands of time samples, and tens of thousands of baselines for arrays with hundreds of antennas. In this paper, we present a fast matrix-based method for simulating radio interferometric measurements (visibilities) at the necessary scale. We achieve this through judicious use of primary beam interpolation, fast approximations for coordinate transforms, and a vectorised outer product to expand per-antenna quantities to per-baseline visibilities, coupled with standard parallelisation techniques. We validate the results of this method, implemented in the publicly-available matvis code, against a high-precision reference simulator, and explore its computational scaling on a variety of problems.
Keywords
Cite
@article{arxiv.2312.09763,
title = {matvis: A matrix-based visibility simulator for fast forward modelling of many-element 21 cm arrays},
author = {Piyanat Kittiwisit and Steven G. Murray and Hugh Garsden and Philip Bull and Christopher Cain and Aaron R. Parsons and Jackson Sipple and Zara Abdurashidova and Tyrone Adams and James E. Aguirre and Paul Alexander and Zaki S. Ali and Rushelle Baartman and Yanga Balfour and Adam P. Beardsley and Lindsay M. Berkhout and Gianni Bernardi and Tashalee S. Billings and Judd D. Bowman and Richard F. Bradley and Jacob Burba and Steven Carey and Chris L. Carilli and Kai-Feng Chen and Carina Cheng and Samir Choudhuri and David R. DeBoer and Eloy de Lera Acedo and Matt Dexter and Joshua S. Dillon and Scott Dynes and Nico Eksteen and John Ely and Aaron Ewall-Wice and Nicolas Fagnoni and Randall Fritz and Steven R. Furlanetto and Kingsley Gale-Sides and Bharat Kumar Gehlot and Abhik Ghosh and Brian Glendenning and Adelie Gorce and Deepthi Gorthi and Bradley Greig and Jasper Grobbelaar and Ziyaad Halday and Bryna J. Hazelton and Jacqueline N. Hewitt and Jack Hickish and Tian Huang and Daniel C. Jacobs and Alec Josaitis and Austin Julius and MacCalvin Kariseb and Nicholas S. Kern and Joshua Kerrigan and Honggeun Kim and Saul A. Kohn and Matthew Kolopanis and Adam Lanman and Paul La Plante and Adrian Liu and Anita Loots and Yin-Zhe Ma and David H. E. MacMahon and Lourence Malan and Cresshim Malgas and Keith Malgas and Bradley Marero and Zachary E. Martinot and Andrei Mesinger and Mathakane Molewa and Miguel F. Morales and Tshegofalang Mosiane and Abraham R. Neben and Bojan Nikolic and Chuneeta Devi Nunhokee and Hans Nuwegeld and Robert Pascua and Nipanjana Patra and Samantha Pieterse and Yuxiang Qin and Eleanor Rath and Nima Razavi-Ghods and Daniel Riley and James Robnett and Kathryn Rosie and Mario G. Santos and Peter Sims and Saurabh Singh and Dara Storer and Hilton Swarts and Jianrong Tan and Nithyanandan Thyagarajan and Pieter van Wyngaarden and Peter K. G. Williams and Zhilei Xu and Haoxuan Zheng},
journal= {arXiv preprint arXiv:2312.09763},
year = {2025}
}
Comments
24 pages, 10 figures, accepted to RAS Techniques and Instruments, matvis is publicly available at https://github.com/HERA-Team/matvis