BESIII Drift Chamber Tracking with Machine Learning 24th International Conference on Computing in High Energy and Nuclear Physics in Adelaide Yao Zhang*1, Ye Yuan1, Ting Zhou2, Karimi Khozani Hosein1, Liqing Qin2 , Qiumei Ma1, * zhangyao@ihep.ac.cn 1. Institute of High Energy Physics, CAS, Beijing, China 2. Guangxi Normal University, Guilin, China The Beijing Spectrometer III ( BESIII, Fig.1) has been running at the Beijing Electron Positron Collider II (BEPCII) for Tau-Charm physics since 2008. The tracking detector of BESIII is a Multilayer Drift Chamber (MDC). The tracking efficiency for high transverse momentum (pT) is high for charged particles but lower when (pT) < 120MeV/c as shown in Fig. 2. Some of the curling tracks with multi turn (|cosq|<0.2) have bad tracking efficiency and quality as Fig. 3 shows. For multi-turn tracks, the hits from different turns will close to each other and even overlap as Fig. 4 shows. The tracking efficiency is low due to effect of non-first turn’s hits. Network model U-Net is a convolutional networks for pixel level semantic segmentation. (https://arxiv.org/abs/1505.04597) Following is the U-Net model used in this work. Conv 3*3+RelU Max-pool Up-conv 3*3 Copy&concatentate Input event Output mask turn ID To learn the behavior of multi-turn tracks, the training sample should have multi-turn curling tracks which are at small dip angle and various transverse momentum. The particle used for this work are single m- and m+ . The track with transverse momentum less than 120MeV/c will curling in the MDC. Only track of more than one turn have been selected for the training and validation and testing. The momentum, angle and turn number distribution of data set show in the Fig. 6. Data sample The feature set to be the raw time of each hit after correction with the event start time. The cell without hits were set with a default time of 2000ns. The truth turn number from Monte-Carlo simulation used as target. The hits from the first turn were marked as 1. The hits from other turns and cells without hit were set to be the default value of 0. Dummy elements in the matrix were set to be the default values. Prepare for the input data The loss function used in this work is a binary cross entropy(BCE) plus a dice coefficient loss. Where 𝐵𝐶𝐸= −[𝑌log𝑓𝑋+ 1 −𝑌log(1 −𝑓(𝑋)]. Dice coefficient loss as a deep learning loss function for highly unbalanced segmentations. It was chosen because of the unbalanced number of element between 1st turn and non-1st turn. (https://arxiv.org/abs/1707.03237) Dice coefficient = 2|𝑋∩𝑌| 𝑋+|𝑌|, loss=1-(Dice coefficient)+BCE. Where, X is the target matrix and Y is the prediction matrix. So in this work,|X| and |Y| are approximately equal with the number of true first turn’s hits and predicted first turn’s hits respectively,|𝑋| ∩ |𝑌| is approximately equal with the number of hits marked as first turn hits and predicted as first turn hits. Loss function A sample of 10 thousands track have been used for training. The data set was separated into 7500 tracks for training and 2500 tracks for validation. The batch size was 64 and the epoch number was 50. Following are the training loss and model accuracy v.s. epoch number. The training has converged after about 15 epochs for validation data set. Training The efficiency and purity have been evaluated for single m- track with validation sample. The definition of efficiency and purity show as following: Efficiency = 𝑇𝑃 𝑃, Purity= 𝑇𝑃 𝑃′. Where TP is the number of predicted first turn and marked as first by Monte-Carlo truth, P is the number of true first turn hits and P’ is the number of hits predicted as first turn. The ROC curve of the average hit efficiency and purity (Fig. 10) shows that at the threshold of about 0.85 the prediction gives the efficiency and purity are about 91%. Fig.11 shows more than half of the events have efficiency and purity greater than 95%. From the event display (Fig.12) we can see that the efficiency of the hits from