Deep Learning Applied To Hit Classification For The Besiii Drift Chamber

Drift chamber is the main tracking detector for high energy physics experiment BESIII. Due to the high luminosity and high beam intensity, the BESIII drift chamber is suffered from the beam background and electronics noise which represent a computing challenge to the reconstruction software. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data especially for object classification. Here we present a first study of deep learning architectures applied to the real data of BESIII drift chamber to accomplish the hit classification of the background and signal.