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Bayesian estimation of cross-coupling and reflection systematics in 21cm array visibility data

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arxiv 2312.03697 v2 pith:AQ54XWQD submitted 2023-12-06 astro-ph.IM astro-ph.CO

Geoff G. Murphy , Philip Bull , Mario G. Santos , Zara Abdurashidova , Tyrone Adams , James E. Aguirre , Paul Alexander , Zaki S. Ali
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Rushelle Baartman Yanga Balfour Adam P. Beardsley Gianni Bernardi Tashalee Billings Judd D. Bowman Richard F. Bradley Jacob Burba Christopher Cain Steven Carey Chris L. Carilli Carina Cheng David R. DeBoer Eloy de Lera Acedo Matt Dexter Joshua S. Dillon Nico Eksteen John Ely Aaron Ewall-Wice Nicolas Fagnoni Randall Fritz Steven R. Furlanetto Kingsley Gale-Sides Brian Glendenning Deepthi Gorthi Bradley Greig Jasper Grobbelaar Ziyaad Halday Bryna J. Hazelton Jacqueline N. Hewitt Jack Hickish Daniel C. Jacobs Austin Julius MacCalvin Kariseb Nicholas S. Kern Joshua Kerrigan Piyanat Kittiwisit Saul A. Kohn Matthew Kolopanis Adam Lanman Paul La Plante Adrian Liu Anita Loots David Harold Edward MacMahon Lourence Malan Cresshim Malgas Keith Malgas Bradley Marero Zachary E. Martinot Andrei Mesinger Mathakane Molewa Miguel F. Morales Tshegofalang Mosiane Steven G. Murray Abraham R. Neben Bojan Nikolic Hans Nuwegeld Aaron R. Parsons Nipanjana Patra Samantha Pieterse Nima Razavi-Ghods James Robnett Kathryn Rosie Peter Sims Jackson Sipple Craig Smith Hilton Swarts Nithyanandan Thyagarajan Pieter van Wyngaarden Peter K. G. Williams Haoxuan Zheng
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Observations with radio arrays that target the 21-cm signal originating from the early Universe suffer from a variety of systematic effects. An important class of these are reflections and spurious couplings between antennas. We apply a Hamiltonian Monte Carlo sampler to the modelling and mitigation of these systematics in simulated Hydrogen Epoch of Reionisation Array (HERA) data. This method allows us to form statistical uncertainty estimates for both our models and the recovered visibilities, which is an important ingredient in establishing robust upper limits on the Epoch of Reionisation (EoR) power spectrum. In cases where the noise is large compared to the EoR signal, this approach can constrain the systematics well enough to mitigate them down to the noise level for both systematics studied. Incoherently averaging the recovered power spectra can further reduce the noise and improve recovery. Where the noise level is lower than the EoR, our modelling can mitigate the majority of the reflections and coupling with there being only a minor level of residual systematics. Our approach performs similarly to existing filtering/fitting techniques used in the HERA pipeline, but with the added benefit of rigorously propagating uncertainties. In all cases it does not significantly attenuate the underlying signal.

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Cited by 1 Pith paper

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  1. Mitigating residual foregrounds and systematic errors in SKA1-Low AA* EoR observations via Bayesian Gaussian Process Regression

    astro-ph.CO 2026-05 unverdicted novelty 5.0 of 10

    Bayesian GPR recovers the 21cm signal within 2σ credible intervals for most k-modes (0.06 to 1.0 h/Mpc) in SKA1-Low simulations that include realistic residual foregrounds and systematics.

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