Optimizing Sparse RFI Prediction using Deep Learning
Creators: name: Kerrigan, Joshua
name: La Plante, Paul
name: Kohn, Saul
name: Pober, Jonathan C.
name: Aguirre, James
name: Abdurashidova, Zara
name: Alexander, Paul
name: Ali, Zaki S.
name: Balfour, Yanga
name: Beardsley, Adam P.
name: Bernardi, Gianni
name: Bowman, Judd D.
name: Bradley, Richard F.
name: Burba, Jacob
name: Carilli, Chris L.
name: Cheng, Carina
name: DeBoer, David R.
name: Dexter, Matt
name: Acedo, Eloy de Lera
name: Dillon, Joshua S.
name: Estrada, Julia
name: Ewall-Wice, Aaron
name: Fagnoni, Nicolas
name: Fritz, Randall
name: Furlanetto, Steve R.
name: Glendenning, Brian
name: Greig, Bradley
name: Grobbelaar, Jasper
name: Gorthi, Deepthi
name: Halday, Ziyaad
name: Hazelton, Bryna J.
name: Hickish, Jack
name: Jacobs, Daniel C.
name: Julius, Austin
name: Kern, Nicholas
name: Kittiwisit, Piyanat
name: Kolopanis, Matthew
name: Lanman, Adam
name: Lekalake, Telalo
name: Liu, Adrian
name: MacMahon, David
name: Malan, Lourence
name: Malgas, Cresshim
name: Maree, Matthys
name: Martinot, Zachary E.
name: Matsetela, Eunice
name: Mesinger, Andrei
name: Molewa, Mathakane
name: Morales, Miguel F.
name: Mosiane, Tshegofalang
name: Neben, Abraham R.
name: Parsons, Aaron R.
name: Patra, Nipanjana
name: Pieterse, Samantha
name: Razavi-Ghods, Nima
name: Ringuette, Jon
name: Robnett, James
name: Rosie, Kathryn
name: Sims, Peter
name: Smith, Craig
name: Syce, Angelo
name: Thyagarajan, Nithyanandan
name: Williams, Peter K. G.
name: Zheng, Haoxuan
Publisher: name: arXiv
Year: 2019
Subjects: subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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 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" dataset 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$\times 10^{5}$ HERA time-ordered 1024 channeled 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 $F_{2}$ score of 0.75 as applied to our HERA-67 observations.
description: 11 pages, 7 figures