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  1. OA@INAF
  2. PRODOTTI RICERCA INAF
  3. 1 CONTRIBUTI IN RIVISTE (Journal articles)
  4. 1.01 Articoli in rivista
Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12386/29064
Title: Optimizing sparse RFI prediction using deep learning
Authors: 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
Issue Date: 2019
Journal: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 
Number: 488
Issue: 2
First Page: 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
Appears in Collections:1.01 Articoli in rivista

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