In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning
(ML) have begun to merge in the last few years, yielding interesting results. It is worthy to …

We present lattice QCD calculations of the valence parton distribution function (PDF) of pion
employing next-to-next-leading-order (NNLO) perturbative QCD matching. Our calculations …

Neutron star observables like masses, radii, and tidal deformability are direct probes to the
dense matter equation of state (EoS). A novel deep learning method that optimizes an EoS …

The precision with which hadronic vacuum polarization (HVP) is obtained determines how
accurately important observables, such as the muon anomalous magnetic moment a μ or the …

The integration of deep learning techniques and physics-driven designs is reforming the
way we address inverse problems, in which accurate physical properties are extracted from …

Abstract The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD
matter remains a major challenge in the field of nuclear astrophysics. With the …

Reconstructing hadron spectral functions through Euclidean correlation functions are of the
important missions in lattice QCD calculations. However, in a Källen–Lehmann (KL) spectral …

Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon
distributions for nuclei with the same mass number. In recent contrasting experiments of Ru …

We present a method for analytic continuation of retarded Green's functions, including
Euclidean Green's functions computed using lattice QCD. The method is based on …