Pith. sign in

REVIEW 1 cited by

Physics Community Needs, Tools, and Resources for Machine Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.16255 v1 pith:LXJVURZO submitted 2022-03-30 cs.LG gr-qchep-exphysics.ins-det

Philip Harris , Erik Katsavounidis , William Patrick McCormack , Dylan Rankin , Yongbin Feng , Abhijith Gandrakota , Christian Herwig , Burt Holzman
show 13 more authors
Kevin Pedro Nhan Tran Tingjun Yang Jennifer Ngadiuba Michael Coughlin Scott Hauck Shih-Chieh Hsu Elham E Khoda Deming Chen Mark Neubauer Javier Duarte Georgia Karagiorgi Mia Liu
This is my paper
classification cs.LGgr-qchep-exphysics.ins-det
keywords needsphysicscommunitylearningmachineresourcestoolsaccessed
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures

    physics.data-an 2025-08 conditional novelty 5.0 of 10

    A graph neural network (ETX4VELO) reconstructs LHCb VELO tracks with performance comparable to the production 'search by triplet' algorithm while running end to end in the GPU-based first-level trigger, with additiona...

Pith tools