Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi...

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Veröffentlicht in:	arXiv.org 2019-02
Hauptverfasser:	Allen, Gabrielle, Andreoni, Igor, Bachelet, Etienne, Berriman, G Bruce, Bianco, Federica B, Biswas, Rahul, Matias Carrasco Kind, Chard, Kyle, Cho, Minsik, Cowperthwaite, Philip S, Etienne, Zachariah B, George, Daniel, Gibbs, Tom, Graham, Matthew, Gropp, William, Gupta, Anushri, Haas, Roland, Huerta, E A, Jennings, Elise, Katz, Daniel S, Khan, Asad, Kindratenko, Volodymyr, Kramer, William T C, Liu, Xin, Mahabal, Ashish, McHenry, Kenton, Miller, J M, Neubauer, M S, Oberlin, Steve, Olivas, Alexander R, Rosofsky, Shawn, Ruiz, Milton, Saxton, Aaron, Schutz, Bernard, Schwing, Alex, Seidel, Ed, Shapiro, Stuart L, Shen, Hongyu, Shen, Yue, Sipőcz, Brigitta M, Sun, Lunan, Towns, John, Tsokaros, Antonios, Wei, Wei, Wells, Jack, Williams, Timothy J, Xiong, Jinjun, Zhao, Zhizhen
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Astronomy Astrophysics Big Data Computer simulation Data management Deep learning Gravitational waves Harnesses Real time Signal processing
Online-Zugang:	Volltext
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