A Pareto-front selection over mass and r-process abundance objectives picks machine-learning nuclear mass models with narrower abundance spreads and more physical neutron separation energy trends.
, N} : fj(⃗u) < fj(⃗v) (2) To apply this method to ML-based mass model selec- tion, each vector represents the predicted masses across the entire nuclear chart
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Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
A Pareto-front selection over mass and r-process abundance objectives picks machine-learning nuclear mass models with narrower abundance spreads and more physical neutron separation energy trends.