Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12
This work addresses track reconstruction inefficiencies in particle physics experiments like CLAS12, representing an incremental improvement to existing tracking algorithms.
The authors tackled the problem of missing track segments in drift chambers for the CLAS12 experiment by using auto-encoders to reconstruct these segments, achieving an accuracy of approximately 0.35 wires and recovering missing tracks with over 99.8% accuracy.
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. Auto encoders are used to reconstruct missing segments from track trajectory. Implemented neural network was able to reliably reconstruct missing segment positions with accuracy of $\approx 0.35$ wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$.