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Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12
In this article we describe the development of machine learning models to assist the CLAS12 tracking algorithm by identifying tracks through inferring missing segments in the drift chambers.
Using Machine Learning for Particle Track Identification in the CLAS12 Detector
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments.
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