Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization
Creators: name: Pagano, Michael
name: Liu, Jing
name: Liu, Adrian
name: Kern, Nicholas S.
name: Ewall-Wice, Aaron
name: Bull, Philip
name: Pascua, Robert
name: Ravanbakhsh, Siamak
name: Abdurashidova, Zara
name: Adams, Tyrone
name: Aguirre, James E.
name: Alexander, Paul
name: Ali, Zaki S.
name: Baartman, Rushelle
name: Balfour, Yanga
name: Beardsley, Adam P.
name: Bernardi, Gianni
name: Billings, Tashalee S.
name: Bowman, Judd D.
name: Bradley, Richard F.
name: Burba, Jacob
name: Carey, Steven
name: Carilli, Chris L.
name: Cheng, Carina
name: DeBoer, David R.
name: Acedo, Eloy de Lera
name: Dexter, Matt
name: Dillon, Joshua S.
name: Eksteen, Nico
name: Ely, John
name: Fagnoni, Nicolas
name: Fritz, Randall
name: Furlanetto, Steven R.
name: Gale-Sides, Kingsley
name: Glendenning, Brian
name: Gorthi, Deepthi
name: Greig, Bradley
name: Grobbelaar, Jasper
name: Halday, Ziyaad
name: Hazelton, Bryna J.
name: Hewitt, Jacqueline N.
name: Hickish, Jack
name: Jacobs, Daniel C.
name: Julius, Austin
name: Kariseb, MacCalvin
name: Kerrigan, Joshua
name: Kittiwisit, Piyanat
name: Kohn, Saul A.
name: Kolopanis, Matthew
name: Lanman, Adam
name: La Plante, Paul
name: Loots, Anita
name: MacMahon, David Harold Edward
name: Malan, Lourence
name: Malgas, Cresshim
name: Malgas, Keith
name: Marero, Bradley
name: Martinot, Zachary E.
name: Mesinger, Andrei
name: Molewa, Mathakane
name: Morales, Miguel F.
name: Mosiane, Tshegofalang
name: Neben, Abraham R.
name: Nikolic, Bojan
name: Nuwegeld, Hans
name: Parsons, Aaron R.
name: Patra, Nipanjana
name: Pieterse, Samantha
name: Razavi-Ghods, Nima
name: Robnett, James
name: Rosie, Kathryn
name: Sims, Peter
name: Smith, Craig
name: Swarts, Hilton
name: Thyagarajan, Nithyanandan
name: van Wyngaarden, Pieter
name: Williams, Peter K. G.
name: Zheng, Haoxuan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that capable of inpainting RFI corrupted data in interferometric instruments. We train our network on simulated data and show that our network is capable at inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modeling are best suited for inpainting over narrowband RFI. We also show that with our fiducial parameters Discrete Prolate Spheroidal Sequences (DPSS) and CLEAN provide the best performance for intermittent ``narrowband'' RFI while Gaussian Progress Regression (GPR) and Least Squares Spectral Analysis (LSSA) provide the best performance for larger RFI gaps. However we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We find these results to be consistent in both simulated and real visibilities. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that in the future, as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities of HERA data.
description: 21 pages, 13 figures