Physics Community Needs, Tools, and Resources for Machine Learning

Harris, Philip; Katsavounidis, Erik; McCormack, William Patrick; Rankin, Dylan; Feng, Yongbin; Gandrakota, Abhijith; Herwig, Christian; Holzman, Burt; Pedro, Kevin; Tran, Nhan; Yang, Tingjun; Ngadiuba, Jennifer; Coughlin, Michael; Hauck, Scott; Hsu, Shih-Chieh; Khoda, Elham E.; Chen, Deming; Neubauer, Mark; Duarte, Javier; Karagiorgi, Georgia; Liu, Mia

doi:10.2172/1873720

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DOE OSTI · 1873720

Physics Community Needs, Tools, and Resources for Machine Learning

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Abstract

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS 97 MATHEMATICS AND COMPUTING

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Harris, Philip, Katsavounidis, Erik, McCormack, William Patrick, Rankin, Dylan, Feng, Yongbin, Gandrakota, Abhijith, Herwig, Christian, Holzman, Burt, Pedro, Kevin, Tran, Nhan, Yang, Tingjun, Ngadiuba, Jennifer, Coughlin, Michael, Hauck, Scott, Hsu, Shih-Chieh, Khoda, Elham E., Chen, Deming, Neubauer, Mark, Duarte, Javier, Karagiorgi, Georgia, Liu, Mia. 2022-03-30. Physics Community Needs, Tools, and Resources for Machine Learning. https://doi.org/10.2172/1873720

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