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Title Machine learning-based jet and event classification at the Electron-Ion Collider and applications to hadron structure and spin physics
Authors James Mulligan, Felix Ringer, Keith Lee, Feng Yuan, M. Ploskon
JLAB number JLAB-THY-22-3739
LANL number arXiv:2210.06450
Other number DOE/OR/23177-5633; MIT-CTP 5473; YITP-SB-2022-34
Document Type(s) (Journal Article) 
Associated with EIC: Yes
Supported by Jefferson Lab LDRD Funding: No
Funding Source: Nuclear Physics (NP)

Compiled for Journal of High Energy Physics
Page(s) 85
Publication 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 momentum requirements. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet.
Experiment Numbers: other
Document: pdf
Accepted Manuscript: JHEP03(2023)085.pdf
Supporting Documents:
Supporting Datasets: