Optimizing sparse RFI prediction using deep learning

J Kerrigan; P la Plante; S Kohn; JC Pober; J Aguirre; Z Abdurashidova; P Alexander; ZS Ali; Y Balfour; AP Beardsley; G Bernardi; JD Bowman; RF Bradley; J Burba; CL Carilli; C Cheng; DR DeBoer; M Dexter; E de Lera Acedo; JS Dillon; J Estrada; A Ewall-Wice; N Fagnoni; R Fritz; SR Furlanetto; B Glendenning; B Greig; J Grobbelaar; D Gorthi; Z Halday; BJ Hazelton; J Hickish; DC Jacobs; A Julius; NS Kern; P Kittiwisit; M Kolopanis; A Lanman; T Lekalake; A Liu; D MacMahon; L Malan; C Malgas; M Maree; ZE Martinot; E Matsetela; A Mesinger; M Molewa; MF Morales; T Mosiane; AR Neben; AR Parsons; N Patra; S Pieterse; N Razavi-Ghods; J Ringuette; J Robnett; K Rosie; P Sims; C Smith; A Syce; N Thyagarajan; PKG Williams; H Zheng

Journal article

Optimizing sparse RFI prediction using deep learning

J Kerrigan, P la Plante, S Kohn, JC Pober, J Aguirre, Z Abdurashidova, P Alexander, ZS Ali, Y Balfour, AP Beardsley, G Bernardi, JD Bowman, RF Bradley, J Burba, CL Carilli, C Cheng, DR DeBoer, M Dexter, E de Lera Acedo, JS Dillon Show all

Monthly Notices of the Royal Astronomical Society | OXFORD UNIV PRESS | Published : 2019

DOI: 10.1093/mnras/stz1865

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 interferom..

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University of Melbourne Researchers

Bradley Greig Author

Related Projects (1)

ARC CENTRE OF EXCELLENCE FOR ALL SKY ASTROPHYSICS IN 3D (ASTRO 3D)

Grants

Awarded by Department of Science and Technology, Ministry of Science and Technology, India

Funding Acknowledgements

This material is based upon work supported by the National Science Foundation under grant nos 1636646 and 1836019 and institutional support from the HERA collaboration partners. This research is funded in part by the Gordon and Betty Moore Foundation. HERA is hosted by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Technology. This work was supported by the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation grant number ACI-1548562 (Towns et al. 2014). Specifically, it made use of the Bridges system, which is supported by National Science Foundation award number ACI-1445606, at the Pittsburgh Supercomputing Center (Nystrom et al. 2015). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. SAK is supported by a University of Pennsylvania SAS Dissertation Completion Fellowship. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO3D), through project number CE170100013. GB acknowledges support from the Royal Society and the Newton Fund under grant NA150184. This work is based on research supported in part by the National Research Foundation of South Africa (grant no. 103424). GB acknowledges funding from the INAF PRIN-SKA 2017 project 1.05.01.88.04 (FORECaST). We acknowledge the support from the Ministero degli Affari Esteri della Cooperazione Internazionale - Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza ZA18GR02. This work is based on research supported by the National Research Foundation of South Africa (grant number 113121).