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Constraining nuclear mass models using ๐‘Ÿ-process observables with multiobjective optimization

Li, Mengke [University of California, Berkeley, CA (United States); University of Notre Dame, IN (United States)] (ORCID:0000000263737494)ยทMumpower, Matthew Ryan [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000299509688)ยทVassh, Nicole [TRIUMF, Vancouver, BC (Canada)] (ORCID:0000000233054326)ยทPorter, William Samuel [University of Notre Dame, IN (United States)] (ORCID:0000000251723298)ยทSurman, Rebecca [University of Notre Dame, IN (United States)] (ORCID:0000000247298823)
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Abstract

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach 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 (๐‘Ÿ process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate ๐‘Ÿ-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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73 NUCLEAR PHYSICS AND RADIATION PHYSICS79 ASTRONOMY AND ASTROPHYSICSBinding energy & massesNuclear astrophysicsNucleosynthesis in explosive environmentsr process

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Li, Mengke [University of California, Berkeley, CA (United States); University of Notre Dame, IN (United States)] (ORCID:0000000263737494), Mumpower, Matthew Ryan [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000299509688), Vassh, Nicole [TRIUMF, Vancouver, BC (Canada)] (ORCID:0000000233054326), Porter, William Samuel [University of Notre Dame, IN (United States)] (ORCID:0000000251723298), Surman, Rebecca [University of Notre Dame, IN (United States)] (ORCID:0000000247298823). 2025-09-11. Constraining nuclear mass models using ๐‘Ÿ-process observables with multiobjective optimization. https://doi.org/10.1103/3x9p-64w7

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