Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

TL;DR

This paper reports precise measurements of neutron-rich Ru and Pd isotopes and demonstrates how these data improve machine learning models predicting nuclear masses, advancing nuclear physics and astrophysics research.

Contribution

It provides highly accurate mass measurements of specific isotopes and integrates these data into a Bayesian-updated machine learning model for nuclear mass prediction.

Findings

Mass measurements improved by an order of magnitude.

New data enhance the accuracy of machine learning mass models.

Bayesian updating with new data refines model predictions.

Abstract

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics and fundamental symmetries. The long-standing goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of $^{108}$Ru, $^{110}$Ru and $^{116}$Pd were measured to a relative mass precision $\delta m/m \approx 10^{-8}$ via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. These mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.