Physics Community Needs, Tools, and Resources for Machine Learning

2022/03/30 by Philip Harris, Harris, Philip, E. Katsavounidis +39 · 1 citation
Computer Science · Decision Sciences · Materials Science · #FOS: Computer and information sciences #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantum Computing Algorithms and Architecture #Scientific Computing and Data Management

paper · pdf · doi:10.48550/arxiv.2203.16255

openalex publication_date 2022/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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• Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

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