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http://hdl.handle.net/20.500.12386/29064
Titolo: | Optimizing sparse RFI prediction using deep learning | Autori: | Kerrigan, Joshua La Plante, Paul Kohn, Saul Pober, Jonathan C. Aguirre, James Abdurashidova, Zara Alexander, Paul Ali, Zaki S. Balfour, Yanga Beardsley, Adam P. BERNARDI, GIANNI Bowman, Judd D. Bradley, Richard F. Burba, Jacob Carilli, Chris L. Cheng, Carina DeBoer, David R. Dexter, Matt Acedo, Eloy de Lera Dillon, Joshua S. Estrada, Julia Ewall-Wice, Aaron Fagnoni, Nicolas Fritz, Randall Furlanetto, Steve R. Glendenning, Brian Greig, Bradley Grobbelaar, Jasper Gorthi, Deepthi Halday, Ziyaad Hazelton, Bryna J. Hickish, Jack Jacobs, Daniel C. Julius, Austin Kern, Nicholas S. Kittiwisit, Piyanat Kolopanis, Matthew Lanman, Adam Lekalake, Telalo Liu, Adrian MacMahon, David Malan, Lourence Malgas, Cresshim Maree, Matthys Martinot, Zachary E. Matsetela, Eunice Mesinger, Andrei Molewa, Mathakane Morales, Miguel F. Mosiane, Tshegofalang Neben, Abraham R. Parsons, Aaron R. Patra, Nipanjana Pieterse, Samantha Razavi-Ghods, Nima Ringuette, Jon Robnett, James Rosie, Kathryn Sims, Peter Smith, Craig Syce, Angelo Thyagarajan, Nithyanandan Williams, Peter K. G. Zheng, Haoxuan |
Data pubblicazione: | 2019 | Rivista: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Numero: | 488 | Fascicolo: | 2 | Da pagina:: | 2605 | Abstract: | Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known `ground truth' data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6 × 10<SUP>5</SUP> HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F<SUB>2</SUB> score of 0.75 as applied to our HERA-67 observations. | URI: | http://hdl.handle.net/20.500.12386/29064 | URL: | https://academic.oup.com/mnras/article/488/2/2605/5529408 | ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stz1865 | Bibcode ADS: | 2019MNRAS.488.2605K | Fulltext: | open |
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