arXiv · 2506.06464
Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Abstract
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which generate r-process abundances aligning with both Solar and stellar data, is a promising method to select ML models with reliable extrapolation power.
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Mengke Li, Matthew Mumpower, Nicole Vassh, William Samuel Porter, Rebecca Surman. 2025-06-06. Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization. https://arxiv.org/abs/2506.06464
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