Vision Calorimeter for Anti-neutron Reconstruction: A Baseline

• URL: http://arxiv.org/abs/2408.10599v1
• Date: Tue, 20 Aug 2024 07:14:28 GMT
• Title: Vision Calorimeter for Anti-neutron Reconstruction: A Baseline
• Authors: Hongtian Yu, Yangu Li, Mingrui Wu, Letian Shen, Yue Liu, Yunxuan Song, Qixiang Ye, Xiaorui Lyu, Yajun Mao, Yangheng Zheng, Yunfan Liu,
• Abstract summary: Vision Calorimeter (ViC) is a baseline method for anti-neutron reconstruction using deep learning detectors. ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81%. This study for the first time realizes the measurement of incident $barn$ momentum.
• Score: 32.87485708592552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In high-energy physics, anti-neutrons ($\bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles. However, this confronts significant challenges instrumentally with the electromagnetic calorimeter (EMC), a typical experimental sensor but recovering the information of incident $\bar{n}$ insufficiently. In this study, we introduce Vision Calorimeter (ViC), a baseline method for anti-neutron reconstruction that leverages deep learning detectors to analyze the implicit relationships between EMC responses and incident $\bar{n}$ characteristics. Our motivation lies in that energy distributions of $\bar{n}$ samples deposited in the EMC cell arrays embody rich contextual information. Converted to 2-D images, such contextual energy distributions can be used to predict the status of $\bar{n}$ ($i.e.$, incident position and momentum) through a deep learning detector along with pseudo bounding boxes and a specified training objective. Experimental results demonstrate that ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81% (from 17.31$^{\circ}$ to 9.90$^{\circ}$). More importantly, this study for the first time realizes the measurement of incident $\bar{n}$ momentum, underscoring the potential of deep learning detectors for particle reconstruction. Code is available at https://github.com/yuhongtian17/ViC.

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