BESIII Drift Chamber Tracking with Machine Learning.

The tracking efficiency and the quality for the drift chamber of the BESIII experiment is essential to the physics analysis. The tracking efficiency of the drift chamber of BESIII is high for the high momentum tracks but still have room to improve for the low momentum tracks, especially for the tracks with multiple turn. A novel way to use a convolutional network called U-Net network is represented to solve the identification of the first turn's hits for the multiple-turn tracks. [ABSTRACT FROM AUTHOR]