arXiv subjects

Jose M. Munoz

Publications and source records attributed to Jose M. Munoz.

4 recordsLinked to original sources

arXiv2026

Linking Electromagnetic Moments to Nuclear Interactions with a Global Physics-Driven Machine-Learning Emulator

Understanding how specific components of the nuclear interaction shape observable properties of atomic nuclei remains a central challenge in nuclear structure research. While previous studies have focused on bulk observables such as nuclear energies and charge radii, it is unclear how distinct operator components of nuclear interactions impact complementary observables such as nuclear electromagnetic moments. Here, we develop a global, physics-constrained emulator to establish a quantitative link between electromagnetic moments and components of chiral nuclear forces. Unlike traditional sensitivity analyses that vary low-energy constants independently, we quantify parameter contributions while accounting for correlations within the physically supported parameter manifold. We show that, unlike bulk observables, electromagnetic moments probe complementary spin and isospin sectors of the interaction and exhibit a pronounced isotope-dependent sensitivity. These developments enable a quantitative assessment of the importance of prospective measurements, providing predictions with quantified uncertainties for observables that may be beyond the current experimental reach.

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arXiv2025

Global Framework for Emulation of Nuclear Calculations

We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.

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arXiv2024

Discovering Nuclear Models from Symbolic Machine Learning

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

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arXiv2022

Predicting Beta Decay Energy with Machine Learning

$Q_β$ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the $β$-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify uncertainty and atomic number as the most relevant characteristics to predict $Q_β$ energies. Furthermore, physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.

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