Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/35136
Title: | Characterization of inpaint residuals in interferometric measurements of the epoch of reionization | Authors: | Pagano, Michael Liu, Jing Liu, Adrian Kern, Nicholas S. Ewall-Wice, Aaron BULL, PHILIP Pascua, Robert Ravanbakhsh, Siamak Abdurashidova, Zara Adams, Tyrone Aguirre, James E. Alexander, Paul Ali, Zaki S. Baartman, Rushelle Balfour, Yanga Beardsley, Adam P. BERNARDI, Gianni Billings, Tashalee S. Bowman, Judd D. Bradley, Richard F. Burba, Jacob Carey, Steven Carilli, Chris L. Cheng, Carina DeBoer, David R. de Lera Acedo, Eloy Dexter, Matt Dillon, Joshua S. Eksteen, Nico Ely, John Fagnoni, Nicolas Fritz, Randall Furlanetto, Steven R. Gale-Sides, Kingsley Glendenning, Brian Gorthi, Deepthi Greig, Bradley Grobbelaar, Jasper Halday, Ziyaad Hazelton, Bryna J. Hewitt, Jacqueline N. Hickish, Jack Jacobs, Daniel C. Julius, Austin Kariseb, MacCalvin Kerrigan, Joshua Kittiwisit, Piyanat Kohn, Saul A. Kolopanis, Matthew Lanman, Adam La Plante, Paul Loots, Anita MacMahon, David Harold Edward Malan, Lourence Malgas, Cresshim Malgas, Keith Marero, Bradley Martinot, Zachary E. Mesinger, Andrei Molewa, Mathakane Morales, Miguel F. Mosiane, Tshegofalang Neben, Abraham R. Nikolic, Bojan Nuwegeld, Hans Parsons, Aaron R. Patra, Nipanjana Pieterse, Samantha Razavi-Ghods, Nima Robnett, James Rosie, Kathryn Sims, Peter Smith, Craig Swarts, Hilton Thyagarajan, Nithyanandan van Wyngaarden, Pieter Williams, Peter K. G. Zheng, Haoxuan |
Issue Date: | 2023 | Journal: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Number: | 520 | Issue: | 4 | First Page: | 5552 | Abstract: | To mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, 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 is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (DPSS) and CLEAN provide the best performance for intermittent 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 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 as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities. | URI: | http://hdl.handle.net/20.500.12386/35136 | URL: | https://academic.oup.com/mnras/article/520/4/5552/7034350 http://arxiv.org/abs/2210.14927v2 |
ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stad441 | Bibcode ADS: | 2023MNRAS.520.5552P | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
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pagano23.pdf | PDF editoriale | 6.15 MB | Adobe PDF | View/Open |
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