Predicting nuclear masses with high precision remains a central challenge in nuclear physics, with implications ranging from stellar nucleosynthesis to determining the boundaries of the nuclear landscape. Approaches using artificial intelligence (AI) have recently achieved remarkable accuracy on this task, but their us...
Kate A. Richardson, S. Trifinopoulos, Mike Williams· Communications AI & Computin...· 3 citations
Hold-out ensembles based on NuCLR (Nuclear Co-Learned Representations), a multi-task model of nuclear data, are developed to study charge radii and electric-quadrupole transition strengths and error bars estimate the expected prediction accuracy across the nuclear chart.
G. Giacalone, S. Trifinopoulos, Mike Williams· 0 citations
We study how virtual next-to-leading-order electroweak corrections affect the quantum observables of entanglement and magic in lepton--lepton collisions. Treating the outgoing particles as multi-qudit systems, $\textit{i.e.}$, qubits for spin-$\tfrac{1}{2}$ fermions and qutrits for massive vector bosons, we compute ent...
Spencer Chang, T. Driscoll, S. Trifinopoulos· 1 citation
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