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Particle track reconstruction is the most computationally intensive processin nuclear physics experiments. Traditional algorithms use a combinatorialapproach that exhaustively tests track measurements ("hits") to identify thosethat form an actual particle trajectory. In this article, we describe thedevelopment of four machine learning (ML) models that assist the trackingalgorithm by identifying valid track candidates from the measurements in driftchambers. Several types of machine learning models were tested, including:Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), ExtremelyRandomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of thiswork, an MLP network classifier was implemented as part of the CLAS12reconstruction software to provide the tracking code with recommended trackcandidates. The resulting software achieved accuracy of greater than 99\% andresulted in an end-to-end speedup of 35\% compared to existing algorithms.

Using Machine Learning for Particle Track Identification in the CLAS12 Detector