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2022

Identifying quenched jets in heavy ion collisions with machine learning

Liu, Lihan, Velkovska, Julia, Verweij, Marta

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Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma.

Modifications of the hard substructure of jets can be explored with modern data-driven techniques.
In this study, a machine learning approach to the identification of quenched jets is designed.
Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8.

Identifying quenched jets in heavy ion collisions with machine learning · Around

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Y. Cui, M. Jia, T. Lin, Y. Song, and S. J. Belongie, Class-balanced loss based on effective number of samples
Original
1901
Earlier work this paper cites.
ATLAS Collaboration, Measurement of jet-substructure observables in top quark, W W boson and light jet production in proton-proton collisions at s = 13 \sqrt{s}=13 TeV with the ATLAS detector
Original
1903
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Jet grooming through reinforcement learning
Original
1903
Earlier work this paper cites.
ALICE Collaboration, Measurement of jet radial profiles in Pb – \textendash Pb collisions at s NN = \sqrt{s_{\rm NN}}= 2.76 TeV
Original
1904
Earlier work this paper cites.
ATLAS Collaboration, Measurement of angular and momentum distributions of charged particles within and around jets in Pb+Pb and p p pp collisions at s NN = 5.02 \sqrt{s_{\mathrm{NN}}}=5.02 TeV with the ATLAS detector
Original
1908
Earlier work this paper cites.
ATLAS Collaboration, Measurement of soft-drop jet observables in p p pp collisions with the ATLAS detector at s \sqrt{s} =13 TeV
Original
1912
Earlier work this paper cites.
CMS Collaboration, Study of high- p T p_{\mathrm{T}} charged particle suppression in PbPb compared to pp collisions at s NN = 2.76 \sqrt{s_{\mathrm{NN}}}=2.76 TeV
Original
1945
Earlier work this paper cites.
CMS Collaboration, Observation and studies of jet quenching in PbPb collisions at nucleon-nucleon center-of-mass energy = 2.76 TeV
Original
1957
Earlier work this paper cites.
ATLAS Collaboration, Measurement of the jet radius and transverse momentum dependence of inclusive jet suppression in lead-lead collisions at s NN = 2.76 \sqrt{s_{\mathrm{NN}}}=2.76 TeV with the ATLAS detector
Original
1967
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, Long short-term memory
1997
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, Better jet clustering algorithms
1997
Earlier work this paper cites.
PHENIX Collaboration, Suppression of hadrons with large transverse momentum in central Au+Au collisions at s NN \sqrt{s_{\mathrm{NN}}} = 130 GeV
2002
Earlier work this paper cites.
STAR Collaboration, Centrality dependence of high p T p_{T} hadron suppression in Au+Au collisions at s N N \sqrt{s}_{NN} = 130-GeV
2002
Earlier work this paper cites.
PHENIX Collaboration, Suppressed π 0 \pi^{0} production at large transverse momentum in central Au+Au collisions at s NN \sqrt{s_{\mathrm{NN}}} = 200 GeV
2003
Earlier work this paper cites.
STAR Collaboration, Transverse momentum and collision energy dependence of high p T p_{\mathrm{T}} hadron suppression in Au+Au collisions at ultrarelativistic energies
2003
Earlier work this paper cites.
STAR Collaboration, Disappearance of back-to-back high p T p_{T} hadron correlations in central Au+Au collisions at s N N \sqrt{s_{NN}} = 200-GeV
2003
Earlier work this paper cites.
ATLAS Collaboration, Measurement of the Lund Jet Plane Using Charged Particles in 13 TeV Proton-Proton Collisions with the ATLAS Detector
Original
2004
Earlier work this paper cites.
J. Mulligan and M. Ploskon, Identifying groomed jet splittings in heavy-ion collisions
Original
2006
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Earlier work this paper cites.

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https://github.com/ustcllh/JetML

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