Using Machine Learning for Particle Track Identification in the CLAS12 Detector

TL;DR

This paper explores the application of various machine learning models to improve particle track identification in the CLAS12 detector, achieving high accuracy and significant speedup over traditional methods.

Contribution

The study introduces four ML models for track candidate identification and implements an MLP classifier into the CLAS12 software, enhancing efficiency and accuracy.

Findings

ML models achieved over 99% accuracy in track identification

MLP classifier integrated into software improves speed by 35%

Multiple ML architectures tested for optimal performance

Abstract

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99\% and resulted in an end-to-end speedup of 35\% compared to existing algorithms.