nucl-th digest · Sep 19, 2025

Refining the deep sub-barrier 12C+12C excitation function with STELLA

Purpose: We have investigated the cross section around the lowest direct coincident gamma-particle measurements, where previously only limits could be established with the aim of obtaining a detailed description of the excitation function. We have furthermore analysed the ratio of extreme decay branching into the first excited state of daughter nuclei with alpha or proton emission at relative kinetic energies where previous measurements are in disagreement with each other. Conclusions: Our findings in the astrophysics RoI support reaction-rate models with a lower average S-factor trend, that deviates significantly from standard extrapolations between 2.2 MeV and 2.6 MeV, for stellar carbon burning simulations of up to 25 Msol stars. Based on our data, an overall increase of the S-factor at deep subbarrier energy cannot be confirmed. The extremely low ratio of the branching into the first excited state with proton over alpha emission of ~ 2% at 3.23 MeV might indicate the presence of alpha cluster compound states in 24Mg. This highly favours {\alpha} emission with fundamental consequences in possible stellar carbon burning sites.

↳ nucl-exastro-ph.HEastro-ph.SRnucl-thPRC(2025)·3 citations

Melting of heavy quarkonia in QGP using deep neural networks

Mohammad Yousuf Jamal🇨🇳 · Fu-Peng Li🇨🇳 · Long-Gang Pang🇨🇳 · Guang-You Qin🇨🇳

Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the temperature-dependent behavior of the screening mass $m_{D} (T)$ and the strong coupling constant $α_{s} (T)$ within a quark-gluon plasma medium. These medium-sensitive quantities are subsequently employed to compute the thermal widths $Γ_{n} (T)$ and binding energies $E_{B} (T)$ of heavy quarkonia states, specifically charmonia and bottomonia, by numerically solving the Schrödinger equation with medium-modified heavy quark potentials. To estimate the dissociation temperatures $T_{d}$ of various quarkonia states, we employ two complementary dissociation criteria: the conventional one, where $2 E_{B} (T_{d}) = Γ_{n} (T_{d})$ , and an additional lower bound criterion defined by $E_{B} (T_{d}) = 3 T_{d}$ . This dual-criterion approach provides a more constrained and physically motivated estimate of the temperature range over which quarkonia states dissolve in the QGP environment. Our machine learning-enhanced predictions show excellent agreement with available lattice QCD results, especially for the ground states $Υ (1 S)$ and $J / ψ$ , and offer new perspectives on the sequential suppression pattern detected in relativistic heavy-ion collision experiments. Overall, this work advances the quantitative description of quarkonium suppression and demonstrates the prospect of modern machine learning methods to bridge theoretical predictions and experimental observations, thereby contributing significantly to QGP tomography.

↳ hep-phnucl-thPRC(2026)·3 citations

Friday·September 19, 2025

Refining the deep sub-barrier 12C+12C excitation function with STELLA

Melting of heavy quarkonia in QGP using deep neural networks

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