Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
Aaijet al.(LHCb), JHEP07, 069 (2023), arXiv:2212.12664 [hep-ex]
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The authors construct and publicly release the TQ4Q2.0 fragmentation functions for all-heavy S-wave tetraquarks via NRQCD factorization, extending prior work with nonconstituent contributions and replica-based uncertainties.
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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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All-charm tetraquarks at hadron colliders: A high-precision fragmentation perspective
The authors construct and publicly release the TQ4Q2.0 fragmentation functions for all-heavy S-wave tetraquarks via NRQCD factorization, extending prior work with nonconstituent contributions and replica-based uncertainties.