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Holographic complex potential of a quarkonium from deep learning

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arxiv 2406.06285 v2 pith:LOLK5GAR submitted 2024-06-10 hep-ph hep-th

classification hep-phhep-th
keywords potentialquarkoniumdeeplearningcomplexcomponentimaginaryunderstanding
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Utilizing an emergent metric developed from deep learning techniques, we determine the complex potential associated with static quarkonium. This study explores the disintegration process of quarkonium by analyzing the real component of this potential, which is crucial for understanding its stability in various conditions. We show that the dissociation length, the critical distance at which a quark and antiquark pair disintegrate, decreases as the temperature increases. Furthermore, our assessment of the imaginary component of the potential indicates an increase in the magnitude of the imaginary potential for quarkonium as temperatures rise. This enhancement contributes to the quarkonium's suppression within the quark-gluon plasma, mirroring the anticipated outcomes from QCD. Our findings not only confirm the theoretical predictions but also demonstrate the efficacy of deep learning methods in advancing our understanding of high-energy particle physics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural network extraction of chromo-electric and chromo-magnetic gluon masses

    hep-ph 2025-07 conditional novelty 5.0 of 10

    A dual neural network quasiparticle model separates electric and magnetic gluon thermal masses from lattice QCD thermodynamics, but the high-temperature mass ratio is imposed by a regularization term.

  2. Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks

    hep-ph 2026-01 reject novelty 3.0 of 10

    A KAN network's fitted polynomials are hand-rearranged into the known Gell-Mann-Okubo mass relations, so the claimed autonomous rediscovery is not demonstrated.

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