# Conflation of Ensemble-Learned Nuclear Mass Models for Enhanced Precision

## TL;DR

This paper introduces a novel ensemble learning approach called ELMA that combines multiple nuclear mass models to significantly improve prediction accuracy, achieving an RMSE below 100 keV and validating results through decay energy evaluations.

## Contribution

The paper presents a new ensemble learning framework that integrates model averaging and correction techniques to enhance nuclear mass predictions beyond existing methods.

## Key findings

- Achieved an RMSE of approximately 65 keV in nuclear mass predictions.
- Demonstrated improved accuracy in $Q$ value predictions for $ ext{α}$ decay.
- Provided a publicly accessible database of nuclear masses and energies for 6,300 nuclei.

## Abstract

Ensemble learning algorithms, the gradient boosting and bagging regressors, are employed to correct the residuals of nuclear mass excess for a diverse set of six nuclear mass models. The weighted average of these corrected residuals reduces due to their partial cancellation, yielding a significant improvement in nuclear mass predictions. Our conflated model, which integrates ensemble learning and model averaging (ELMA), achieves a root mean square error of approximately 65 keV, well below the critical threshold of 100 keV, for the complete data set of Atomic Mass Evaluation (AME2020). The validity of ELMA is demonstrated through the evaluation of $Q$ values for $\alpha$ decay, showing a marked decrease in deviations from experimental data relative to predictions from individual nuclear mass models. We have also compiled a table of nuclear mass excesses and binding energies for about 6,300 nuclei, which serves as a valuable resource for various nuclear physics applications and is publicly accessible via the ELMA web interface (https://ddnp.in).