Using Machine Learning for Particle Track Identification in the CLAS12 Detector
This work addresses a domain-specific bottleneck in nuclear physics experiments by providing an incremental improvement to tracking efficiency and speed.
The paper tackled the computationally intensive problem of particle track reconstruction in nuclear physics experiments by developing machine learning models to identify valid track candidates, achieving over 99% accuracy and a 35% speedup compared to existing algorithms.
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.