Vision Calorimeter for Anti-neutron Reconstruction: A Baseline
<|reference_start|>Vision Calorimeter for Anti-neutron Reconstruction: A Baseline: 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.<|reference_end|>
@article{yu2024vision,
    title={Vision Calorimeter for Anti-neutron Reconstruction: A Baseline},
    author={Hongtian Yu, Yangu Li, Mingrui Wu, Letian Shen, Yue Liu, Yunxuan Song,
  Qixiang Ye, Xiaorui Lyu, Yajun Mao, Yangheng Zheng, Yunfan Liu},
    journal={arXiv preprint arXiv:2408.10599},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10599},
    primaryClass={hep-ex cs.CV}
}