Machine Learning beta-decay Half-lives and Their Application to r-Process Observables

arXiv:2609.30408 · astro-ph.HE, nucl-th · Submitted 2026-09-24 · Read on arXiv

astro-ph.HE, nucl-th

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Abundance — 25× in this paper · explained in 1 episode
• Machine Learning — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Mixture Density Network — 4× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• SHAP values — 2× in this paper · explained in 2 episodes
• Ab Initio — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• Adam: A Method for Stochastic Optimization
• r-Process Nucleosynthesis With Ab Initio Nuclear Masses Around The N=82 Shell Closure
• A Unified Approach to Interpreting Model Predictions
• Large-Scale Calculations of $\beta$-Decay Rates and Implications for $r$-Process Nucleosynthesis
• The N=126 Factory: A New Multi-Nucleon Transfer Reaction Facility

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