Pith. sign in

REVIEW 1 cited by

Foreground modelling via Gaussian process regression: an application to HERA data

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.06041 v2 pith:VNT3ACMB submitted 2020-04-13 astro-ph.CO

Foreground modelling via Gaussian process regression: an application to HERA data

Abhik Ghosh , Florent Mertens , Gianni Bernardi , M\'ario G. Santos , Nicholas S. Kern , Christopher L. Carilli , Trienko L. Grobler , L\'eon V. E. Koopmans
show 65 more authors
Daniel C. Jacobs Adrian Liu Aaron R. Parsons Miguel F. Morales James E. Aguirre Joshua S. Dillon Bryna J. Hazelton Oleg M. Smirnov Bharat K. Gehlot Siyanda Matika Paul Alexander Zaki S. Ali Adam P. Beardsley Roshan K. Benefo Tashalee S. Billings Judd D. Bowman Richard F. Bradley Carina Cheng Paul M. Chichura David R. DeBoer Eloy de Lera Acedo Aaron Ewall-Wice Gcobisa Fadana Nicolas Fagnoni Austin F. Fortino Randall Fritz Steve R. Furlanetto Samavarti Gallardo Brian Glendenning Deepthi Gorthi Bradley Greig Jasper Grobbelaar Jack Hickish Alec Josaitis Austin Julius Amy S. Igarashi MacCalvin Kariseb Saul A. Kohn Matthew Kolopanis Telalo Lekalake Anita Loots David MacMahon Lourence Malan Cresshim Malgas Matthys Maree Zachary E. Martinot Nathan Mathison Eunice Matsetela Andrei Mesinger Abraham R. Neben Bojan Nikolic Chuneeta D. Nunhokee Nipanjana Patra Samantha Pieterse Nima Razavi-Ghods Jon Ringuette James Robnett Kathryn Rosie Raddwine Sell Craig Smith Angelo Syce Max Tegmark Nithyanandan Thyagarajan Peter K. G. Williams Haoxuan Zheng
This is my paper
classification astro-ph.CO
keywords foregroundsignaldataspectrumcmpccomponentscorrupteddominating
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Mitigating gain calibration errors from EoR observations with SKA1-Low AA*

    astro-ph.CO 2025-10 unverdicted novelty 4.0

    Simulations show hybrid foreground mitigation (GPR + PCA combined with avoidance) recovers the HI 21cm signal within 2σ for gain calibration errors ≤1% in SKA1-Low AA* observations over 0.05-0.5 Mpc^{-1} scales.