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 machine
learning models that assist the tracking algorithm by identifying valid track candidates from the measurement
("hits") in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN),
Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN).
As a result of this work the CLAS12 tracking efficiency increased by ~15% for single particle tracking, and
20%-40% gained efficiency in multi-particle final states. The tracking code also increased in speed by 35%.