Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization
| Autor: | Michael Pagano, Jing Liu, Adrian Liu, Nicholas S Kern, Aaron Ewall-Wice, Philip Bull, Robert Pascua, Siamak Ravanbakhsh, Zara Abdurashidova, Tyrone Adams, James E Aguirre, Paul Alexander, Zaki S Ali, Rushelle Baartman, Yanga Balfour, Adam P Beardsley, Gianni Bernardi, Tashalee S Billings, Judd D Bowman, Richard F Bradley, Jacob Burba, Steven Carey, Chris L Carilli, Carina Cheng, David R DeBoer, Eloy de Lera Acedo, Matt Dexter, Joshua S Dillon, Nico Eksteen, John Ely, 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, Joshua Kerrigan, Piyanat Kittiwisit, Saul A Kohn, Matthew Kolopanis, Adam Lanman, Paul La Plante, 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, Abraham R Neben, Bojan Nikolic, Hans Nuwegeld, Aaron R Parsons, Nipanjana Patra, Samantha Pieterse, Nima Razavi-Ghods, James Robnett, Kathryn Rosie, Peter Sims, Craig Smith, Hilton Swarts, Nithyanandan Thyagarajan, Pieter van Wyngaarden, Peter K G Williams, Haoxuan Zheng |
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| Rok vydání: | 2022 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Space and Planetary Science FOS: Physical sciences Astronomy and Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Astrophysics - Cosmology and Nongalactic Astrophysics |
| DOI: | 10.48550/arxiv.2210.14927 |
| Popis: | 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. Comment: 21 pages, 13 figures |
| Databáze: | OpenAIRE |
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