Deep learning assisted jet tomography for the study of Mach cones in QGP

Yang, Zhong; He, Yayun; Chen, Wei; Ke, Wei-Yao; Pang, Long-Gang; Wang, Xin-Nian

doi:10.1140/epjc/s10052-023-11807-1

Deep learning assisted jet tomography for the study of Mach cones in QGP

Journal Article · Sun Jul 23 00:00:00 EDT 2023 · European Physical Journal. C, Particles and Fields (Online)

DOI:https://doi.org/10.1140/epjc/s10052-023-11807-1· OSTI ID:1991996

Yang, Zhong; He, Yayun; Chen, Wei; Ke, Wei-Yao; ; Wang, Xin-Nian

Abstract

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the propagation direction of the parton shower relative to the radial flow because of the distortion caused by the collective expansion of the QGP and the large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and parton-shower propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different path lengths and orientations relative to the radial flow of the QGP in heavy-ion collisions.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC); National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Office of Science (SC), Nuclear Physics (NP)National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: 89233218CNA000001; AC02-05CH11231

OSTI ID:: 1991996

Alternate ID(s):: OSTI ID: 2007197
OSTI ID: 1998143

Report Number(s):: LA-UR--23-29361; 652; PII: 11807

Journal Information:: European Physical Journal. C, Particles and Fields (Online), Journal Name: European Physical Journal. C, Particles and Fields (Online) Journal Issue: 7 Vol. 83; ISSN 1434-6052

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Germany

Language:: English

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