Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B

TL;DR

This paper demonstrates that machine learning techniques, including neural networks and clustering algorithms, significantly improve cluster reconstruction efficiency in the CALIFA calorimeter at R3B, based on Geant4 simulations.

Contribution

It introduces advanced machine learning methods for cluster reconstruction in nuclear physics detectors, surpassing traditional algorithms in efficiency.

Findings

Over 30% improvement in cluster reconstruction efficiency

Effective use of multi-dimensional parameter space including geometry, energy, and time

Edge Detection Neural Network shows significant differences in performance

Abstract

The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance performance, advanced machine learning techniques such as agglomerative clustering were implemented to use the full multi-dimensional parameter space including geometry, energy and time of individual interactions. An Edge Detection Neural Network exhibited significant differences. This study, based on Geant4 simulations, demonstrates improvements in cluster reconstruction efficiency of more than 30%, showcasing the potential of machine learning in nuclear physics experiments.