Machine-Learning-Enabled Measurements of Astrophysical (p,n) Reactions with the SECAR Recoil Separator

TL;DR

This paper introduces a machine learning-based method to adapt the SECAR recoil separator for measuring (p,n) reactions on unstable nuclei, enabling new astrophysical reaction rate data crucial for understanding supernova nucleosynthesis.

Contribution

The authors developed a novel machine learning integrated ion-optical solution for SECAR, allowing the measurement of (p,n) reactions with minimal mass change, which was previously challenging.

Findings

Successful measurement of the $^{58}$Fe(p,n)$^{58}$Co reaction cross-section.

Demonstration of the method's effectiveness in achieving required performance.

Paving the way for studying (p,n) reactions on unstable nuclei at FRIB.

Abstract

The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the $^{58}$Fe(p,n)$^{58}$Co reaction in inverse kinematics with a 3.66$\pm$0.12 MeV/nucleon $^{58}$Fe beam (corresponding to 3.69$\pm$0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3$\pm$6.3 mb and served as a benchmark for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach marks a significant advancement in experimental nuclear astrophysics, as it paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB.