Bottomonium suppression with a machine-learning-informed Debye mass
Abstract
Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex-valued heavy-quark Kent State University (KSU) potential. The resulting complex potential is used to solve the real-time Schrödinger equation within the quantum trajectories (QTraj) framework for the evolution of bottomonium in the quark-gluon plasma. We then compute the nuclear modification factors and double ratios for bottomonium Υ(1S), Υ(2S), and Υ(3S) states in Pb-Pb collisions at √(sNN) = 5.02 TeV. We compare our ML-induced results with those from the original KSU model and with experimental measurements from ALICE, ATLAS, and CMS collaborations. We find that the machine-learned Debye mass leads to improved agreement with data, particularly for excited states, highlighting the utility of machine learning in modeling in-medium QCD effects.
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BibTeX
@misc{islam2026bottomonium,
title = {{Bottomonium suppression with a machine-learning-informed Debye mass}},
author = {Ajaharul Islam and Shibo Chen and Fu-Peng Li and Long-Gang Pang and Guang-You Qin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.09047},
eprint = {2609.09047},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.09047},
}
RIS
TY - UNPB
TI - Bottomonium suppression with a machine-learning-informed Debye mass
AU - Ajaharul Islam
AU - Shibo Chen
AU - Fu-Peng Li
AU - Long-Gang Pang
AU - Guang-You Qin
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2609.09047
UR - https://arxiv.org/abs/2609.09047
AB - Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex-valued heavy-quark Kent State University (KSU) potential. The resulting complex potential is used to solve the real-time Schrödinger equation within the quantum trajectories (QTraj) framework for the evolution of bottomonium in the quark-gluon plasma. We then compute the nuclear modification factors and double ratios for bottomonium Υ(1S), Υ(2S), and Υ(3S) states in Pb-Pb collisions at √(s_NN) = 5.02 TeV. We compare our ML-induced results with those from the original KSU model and with experimental measurements from ALICE, ATLAS, and CMS collaborations. We find that the machine-learned Debye mass leads to improved agreement with data, particularly for excited states, highlighting the utility of machine learning in modeling in-medium QCD effects.
ER -