Optimizing sparse RFI prediction using deep learning

Joshua Kerrigan, Paul la Plante, Saul Kohn, Jonathan C. Pober, James Aguirre, Zara Abdurashidova, Paul Alexander, Zaki S. Ali, Yanga Balfour, Adam P. Beardsley, Gianni Bernardi, Judd D. Bowman, Richard F. Bradley, Jacob Burba, Chris L. Carilli, Carina Cheng, David R. DeBoer, Matt Dexter, Eloy de Lera Acedo, Joshua S. DillonJulia Estrada, Aaron Ewall-Wice, Nicolas Fagnoni, Randall Fritz, Steve R. Furlanetto, Brian Glendenning, Bradley Greig, Jasper Grobbelaar, Deepthi Gorthi, Ziyaad Halday, Bryna J. Hazelton, Jack Hickish, Daniel C. Jacobs, Austin Julius, Nicholas S. Kern, Piyanat Kittiwisit, Matthew Kolopanis, Adam Lanman, Telalo Lekalake, Adrian Liu, David MacMahon, Lourence Malan, Cresshim Malgas, Matthys Maree, Zachary E. Martinot, Eunice Matsetela, Andrei Mesinger, Mathakane Molewa, Miguel F. Morales, Tshegofalang Mosiane, Abraham R. Neben, Aaron R. Parsons, Nipanjana Patra, Samantha Pieterse, Nima Razavi-Ghods, Jon Ringuette, James Robnett, Kathryn Rosie, Peter Sims, Craig Smith, Angelo Syce, Nithyanandan Thyagarajan, Peter K.G. Williams, Haoxuan Zheng

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Fingerprint

Dive into the research topics of 'Optimizing sparse RFI prediction using deep learning'. Together they form a unique fingerprint.

Physics & Astronomy

Earth & Environmental Sciences