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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Remler formalism with in-medium heavy-quark potential describes charmonium yields at SPS energies and supplies estimates for FAIR energies after calibration on p+p and p+A collisions.
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
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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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Charmonium production at SPS and FAIR energies
Remler formalism with in-medium heavy-quark potential describes charmonium yields at SPS energies and supplies estimates for FAIR energies after calibration on p+p and p+A collisions.
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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.