Statistical Methods for Thermonuclear Reaction Rates and Nucleosynthesis Simulations

TL;DR

This paper discusses advanced statistical Monte Carlo methods for accurately estimating thermonuclear reaction rates and nucleosynthesis outcomes, addressing complex nuclear data and uncertainties in astrophysical models.

Contribution

It introduces Monte Carlo techniques for deriving probability distributions of reaction rates and abundances, improving uncertainty quantification in nuclear astrophysics.

Findings

Provides probability density functions for reaction rates and abundances

Demonstrates applications to supernovae, novae, and big bang nucleosynthesis

Enhances statistical rigor in nuclear astrophysics simulations

Abstract

Rigorous statistical methods for estimating thermonuclear reaction rates and nucleosynthesis are becoming increasingly established in nuclear astrophysics. The main challenge being faced is that experimental reaction rates are highly complex quantities derived from a multitude of different measured nuclear parameters (e.g., astrophysical S-factors, resonance energies and strengths, particle and gamma-ray partial widths). We discuss the application of the Monte Carlo method to two distinct, but related, questions. First, given a set of measured nuclear parameters, how can one best estimate the resulting thermonuclear reaction rates and associated uncertainties? Second, given a set of appropriate reaction rates, how can one best estimate the abundances from nucleosynthesis (i.e., reaction network) calculations? The techniques described here provide probability density functions that can be used to derive statistically meaningful reaction rates and final abundances for any desired coverage probability. Examples are given for applications to s-process neutron sources, core-collapse supernovae, classical novae, and big bang nucleosynthesis.