15–20 May 2022
University of Sussex
Europe/London timezone

Performance Study of a New Cluster Splitting Algorithm for the Reconstruction of PANDA EMC Data

Not scheduled
20m
University of Sussex

University of Sussex

Falmer Campus, Brighton, Sussex, BN1 9QH, United Kingdom

Speakers

Zi-Yu Zhang (Nankai University) Guang Zhao (IHEP)Prof. Sheng-Sen Sun (IHEP)

Description

For high momentum pi0 mesons, the angle between the two final-state photons decreases with the increase of the momentum of the pi0, which enhances the probability of overlapping electromagnetic showers. The performance of the cluster splitting algorithm in the EMC reconstruction is crucial for the mass resolution measurement of the pi0 at high momenta. If there are several local maxima in a cluster, it is considered as a superposition of multiple showers. It is necessary to split the cluster according to the number of maxima. The classical cluster splitting algorithm is based on the theoretical lateral distribution of electromagnetic showers which can be described as a (multi-)exponential function. In a realistic electromagnetic calorimeter, considering the granularity of the detector, the measured energy in a cell is actually the integral of the theoretical energy deposition, which deviates from the exponential function. Based on the simulation of the barrel EMC of the PANDA experiment, the cluster splitting algorithm is updated using a new lateral energy measurement function which depends on the dedicated granularity of the detector. The mass resolution of the pi0 has been improved in the high momentum range compared with the previously used method.

Authors

Zi-Yu Zhang (Nankai University) Guang Zhao (IHEP) Prof. Sheng-Sen Sun (IHEP) Mr Qing Pu Chun-Xiu Liu (IHEP) Prof. Chun-Xu Yu (Nankai University)

Co-authors

Prof. Guang-Shun Huang (University of Science and Technology of China) Prof. Bei-Jiang Liu (IHEP) Dr Dong Liu Hang Qi (USTC) Prof. Xiao-Yan Shen (IHEP) Prof. Tobias Stockmanns (GSI) Ms Fei Wang (University of South China) Ms Yi-Tong Zhang (IHEP)

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