STI Publications - View Publication Form #17184

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Publication Information
Title Machine learning-based jet and event classification at the Electron-Ion Collider and applications to hadron structure and spin physics
Abstract We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at the relatively low EIC energies, focusing on (i) identifying the quark flavor of the jet and (ii) identifying the hard-scattering process. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current RHIC program, including the extraction of (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant for the detector design including particle identification, charge information, and minimum transverse mome
Author(s) James Mulligan, Felix Ringer, Kyle Lee, Feng Yuan, M. Ploskon
Publication Date March 2023
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Theory & Comp Physics / THEORY CENTER / THEORY CENTER
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-THY-22-3739 OSTI Number: 1962985
LANL Number: arXiv:2210.06450 Other Number: DOE/OR/23177-5633; MIT-CTP 5473; YITP-SB-2022-34
Associated with an experiment No
Associated with EIC Yes
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Journal of High Energy Physics
Refereed No
Volume
Issue
Page(s) 85
Attachments/Datasets/DOI Link
Document(s)
JHEP03(2023)085.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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