%0 Generic %1 pagano2022characterization %A Pagano, Michael %A Liu, Jing %A Liu, Adrian %A Kern, Nicholas S. %A Ewall-Wice, Aaron %A Bull, Philip %A Pascua, Robert %A Ravanbakhsh, Siamak %A Abdurashidova, Zara %A Adams, Tyrone %A Aguirre, James E. %A Alexander, Paul %A Ali, Zaki S. %A Baartman, Rushelle %A Balfour, Yanga %A Beardsley, Adam P. %A Bernardi, Gianni %A Billings, Tashalee S. %A Bowman, Judd D. %A Bradley, Richard F. %A Burba, Jacob %A Carey, Steven %A Carilli, Chris L. %A Cheng, Carina %A DeBoer, David R. %A Acedo, Eloy de Lera %A Dexter, Matt %A Dillon, Joshua S. %A Eksteen, Nico %A Ely, John %A Fagnoni, Nicolas %A Fritz, Randall %A Furlanetto, Steven R. %A Gale-Sides, Kingsley %A Glendenning, Brian %A Gorthi, Deepthi %A Greig, Bradley %A Grobbelaar, Jasper %A Halday, Ziyaad %A Hazelton, Bryna J. %A Hewitt, Jacqueline N. %A Hickish, Jack %A Jacobs, Daniel C. %A Julius, Austin %A Kariseb, MacCalvin %A Kerrigan, Joshua %A Kittiwisit, Piyanat %A Kohn, Saul A. %A Kolopanis, Matthew %A Lanman, Adam %A La Plante, Paul %A Loots, Anita %A MacMahon, David Harold Edward %A Malan, Lourence %A Malgas, Cresshim %A Malgas, Keith %A Marero, Bradley %A Martinot, Zachary E. %A Mesinger, Andrei %A Molewa, Mathakane %A Morales, Miguel F. %A Mosiane, Tshegofalang %A Neben, Abraham R. %A Nikolic, Bojan %A Nuwegeld, Hans %A Parsons, Aaron R. %A Patra, Nipanjana %A Pieterse, Samantha %A Razavi-Ghods, Nima %A Robnett, James %A Rosie, Kathryn %A Sims, Peter %A Smith, Craig %A Swarts, Hilton %A Thyagarajan, Nithyanandan %A van Wyngaarden, Pieter %A Williams, Peter K. G. %A Zheng, Haoxuan %D 2022 %K library %T Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization %U http://arxiv.org/abs/2210.14927 %X 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.

@misc{pagano2022characterization, abstract = {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.}, added-at = {2022-10-28T14:17:27.000+0200}, author = {Pagano, Michael and Liu, Jing and Liu, Adrian and Kern, Nicholas S. and Ewall-Wice, Aaron and Bull, Philip and Pascua, Robert and Ravanbakhsh, Siamak and Abdurashidova, Zara and Adams, Tyrone and Aguirre, James E. and Alexander, Paul and Ali, Zaki S. and Baartman, Rushelle and Balfour, Yanga and Beardsley, Adam P. and Bernardi, Gianni and Billings, Tashalee S. and Bowman, Judd D. and Bradley, Richard F. and Burba, Jacob and Carey, Steven and Carilli, Chris L. and Cheng, Carina and DeBoer, David R. and Acedo, Eloy de Lera and Dexter, Matt and Dillon, Joshua S. and Eksteen, Nico and Ely, John and Fagnoni, Nicolas and Fritz, Randall and Furlanetto, Steven R. and Gale-Sides, Kingsley and Glendenning, Brian and Gorthi, Deepthi and Greig, Bradley and Grobbelaar, Jasper and Halday, Ziyaad and Hazelton, Bryna J. and Hewitt, Jacqueline N. and Hickish, Jack and Jacobs, Daniel C. and Julius, Austin and Kariseb, MacCalvin and Kerrigan, Joshua and Kittiwisit, Piyanat and Kohn, Saul A. and Kolopanis, Matthew and Lanman, Adam and La Plante, Paul and Loots, Anita and MacMahon, David Harold Edward and Malan, Lourence and Malgas, Cresshim and Malgas, Keith and Marero, Bradley and Martinot, Zachary E. and Mesinger, Andrei and Molewa, Mathakane and Morales, Miguel F. and Mosiane, Tshegofalang and Neben, Abraham R. and Nikolic, Bojan and Nuwegeld, Hans and Parsons, Aaron R. and Patra, Nipanjana and Pieterse, Samantha and Razavi-Ghods, Nima and Robnett, James and Rosie, Kathryn and Sims, Peter and Smith, Craig and Swarts, Hilton and Thyagarajan, Nithyanandan and van Wyngaarden, Pieter and Williams, Peter K. G. and Zheng, Haoxuan}, biburl = {https://www.bibsonomy.org/bibtex/224f7e429b2246e633300b209ce40f825/gpkulkarni}, description = {Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization}, interhash = {9a4142d36013bfe3712c8cdb9133b040}, intrahash = {24f7e429b2246e633300b209ce40f825}, keywords = {library}, note = {cite arxiv:2210.14927Comment: 26 pages, 18 figures}, timestamp = {2022-10-28T14:17:27.000+0200}, title = {Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization}, url = {http://arxiv.org/abs/2210.14927}, year = 2022 }