{"paper":{"title":"Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","cs.LG","gr-qc"],"primary_cat":"astro-ph.IM","authors_text":"Aaron Saxton, Alexander R. Olivas Jr, Alex Schwing, Antonios Tsokaros, Anushri Gupta, Asad Khan, Ashish Mahabal, Bernard Schutz, Brigitta M. Sip\\H{o}cz, Daniel George, Daniel S. Katz, E. A. Huerta, Ed Seidel, Elise Jennings, Etienne Bachelet, Federica B. Bianco, Gabrielle Allen, G. Bruce Berriman, Hongyu Shen, Igor Andreoni, Jack Wells, Jinjun Xiong, J. M. Miller, John Towns, Kenton McHenry, Kyle Chard, Lunan Sun, Matias Carrasco Kind, Matthew Graham, Milton Ruiz, Minsik Cho, M. S. Neubauer, Philip S. Cowperthwaite, Rahul Biswas, Roland Haas, Shawn Rosofsky, Steve Oberlin, Stuart L. Shapiro, Timothy J. Williams, Tom Gibbs, Volodymyr Kindratenko, Wei Wei, William Gropp, William T. C. Kramer, Xin Liu, Yue Shen, Zachariah B. Etienne, Zhizhen Zhao","submitted_at":"2019-02-01T19:02:18Z","abstract_excerpt":"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-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded \"Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale\" wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00522","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}