Abstract
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.
Highlights
1.1 The BESIII experimentThe Beijing Spectrometer III (BESIII[1], Fig.1) has been running at the Beijing Electron Positron Collider II (BEPCII) for Tau-Charm physics since 2008
The track finding of the tracking detector is a problem of pattern recognition which is very suitable for the utilization of the machine learning or deep learning technic
To learn the behavior of the multi-turn tracks, the training sample is the multi-turn curling tracks generated with Monte-Carlo which are at small dip angle and various transverse momentum
Summary
The Beijing Spectrometer III (BESIII[1], Fig.1) has been running at the Beijing Electron Positron Collider II (BEPCII) for Tau-Charm physics since 2008. The tracking detector of the BESIII is a Multilayer Drift Chamber (MDC)[2]. The tracking efficiency for the high transverse momentum is high but lower when the cos (where is the dip angle of the track ) is small for the low transverse momentum tracks (Fig. 2)
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