https://doi.org/10.1140/epjc/s10052-023-11807-1
Regular Article - Theoretical Physics
Deep learning assisted jet tomography for the study of Mach cones in QGP
1
Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, 430079, Wuhan, China
2
Guangdong Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, 510006, Guangzhou, China
3
Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Southern Nuclear Science Computing Center, South China Normal University, 510006, Guangzhou, China
4
College of Science, Wuhan University of Science and Technology, 430065, Wuhan, China
5
Physics Department, University of California, 94720, Berkeley, CA, USA
6
Nuclear Science Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
7
Theoretical Division, Los Alamos National Laboratory, 87545, Los Alamos, NM, USA
Received:
1
March
2023
Accepted:
5
July
2023
Published online:
23
July
2023
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.
© The Author(s) 2023
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