Roadmap on fast machine learning for science

Machine Learning Science and Technology 2026 Open access

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Authors: Sioni Summers, Alexander Tapper, Thea Klaeboe Aarrestad, Chen Qin, Karin Rathsman, M. J. V. Streeter, Charlotte A.J. Palmer, Jonathan Citrin, Changgang Zheng, Noa Zilberman, Alexander Titterton, Tobias Becker
DOI: 10.1088/2632-2153/ae484b
Cited by: 2
References: 95

Abstract

Abstract The need for microsecond speed machine learning (ML) inference for particle physics experiments has emerged in recent years, in particular for the forthcoming upgrades to the experiments at the Large Hadron Collider at CERN. A community has grown around the need to develop the custom hardware platforms and tools required. The material presented in this report is drawn from the latest workshop held by the fast ML for science community and comprises of a collection of perspectives on the status of fast ML in different scientific domains, and the supporting technology.

Fields of Study & Subjects

Computational Physics and Python Applications Particle physics theoretical and experimental studies Particle Detector Development and Performance

Publication details

Journal / source
Machine Learning Science and Technology
Publisher
IOP Publishing
Volume
7
Issue
2
Pages
021501–021501
Publication date
19 February 2026
Language
EN