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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.10599 |
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| _version_ | 1866914292188577792 |
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| author | Yu, Hongtian Li, Yangu Wu, Mingrui Shen, Letian Liu, Yue Song, Yunxuan Ye, Qixiang Lyu, Xiao-Rui Mao, Yajun Zheng, Yangheng Liu, Yunfan |
| author_facet | Yu, Hongtian Li, Yangu Wu, Mingrui Shen, Letian Liu, Yue Song, Yunxuan Ye, Qixiang Lyu, Xiao-Rui Mao, Yajun Zheng, Yangheng Liu, Yunfan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10599 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vision Calorimeter for Anti-neutron Reconstruction: A Baseline Yu, Hongtian Li, Yangu Wu, Mingrui Shen, Letian Liu, Yue Song, Yunxuan Ye, Qixiang Lyu, Xiao-Rui Mao, Yajun Zheng, Yangheng Liu, Yunfan High Energy Physics - Experiment Computer Vision and Pattern Recognition 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. |
| title | Vision Calorimeter for Anti-neutron Reconstruction: A Baseline |
| topic | High Energy Physics - Experiment Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.10599 |