Classification of events from $\alpha$-induced reactions in the MUSIC detector via statistical and ML methods

TL;DR

This paper introduces a novel statistical and machine learning approach for classifying alpha-induced reaction events in MUSIC detector data, significantly reducing manual effort and enabling automated analysis.

Contribution

It presents the first ML-based classification method for MUSIC data, improving efficiency and consistency over traditional expert-driven analysis techniques.

Findings

Classified events agree within ±20% with traditional methods

Reduced manual effort in data analysis

Established foundation for automated event extraction

Abstract

The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the MUSIC data produced in one experiment scientists carefully extract an order of $10^3$ events of interest from about $10^{9}$ total events, where each event can be represented by an 18-dimensional vector. However, the standard data classification process is based on expert-driven, manually intensive data analysis techniques that require several months to identify patterns and classify the relevant events from the collected data. To address this issue, we present a method for the classification of events originating from specific $\alpha$-induced reactions by combining statistical and machine learning methods that require significantly less input from the domain scientist, relative to the standard technique. We applied the new method to two experimental data sets and compared our results with those obtained using traditional methods. With few exceptions, the number of events classified by our method agrees within $\pm20\%$ with the results obtained using traditional methods. With the present method, which is the first of its kind for the MUSIC data, we have established the foundation for the automated extraction of physical events of interest from experiments using the MUSIC detector.