Constraining nuclear mass models using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:math>-process observables with multiobjective optimization

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 (<a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mi>r</a:mi></a:mrow></a:math> 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 <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mrow><b:mi>r</b:mi></b:mrow></b:math>-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.