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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
nucl-th 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Calculations of excitation functions, momentum spectra, and transparency ratios for χ_c1(1P) on 12C and 184W nuclei demonstrate sensitivity to different absorption cross-section scenarios, proposed for extraction via future CEBAF data.
citing papers explorer
-
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.
-
Absorption of 1$P$-wave heavy charmonium $\chi_{c1}(1P)$ in nuclei
Calculations of excitation functions, momentum spectra, and transparency ratios for χ_c1(1P) on 12C and 184W nuclei demonstrate sensitivity to different absorption cross-section scenarios, proposed for extraction via future CEBAF data.