Title: Vision Calorimeter for Anti-neutron Reconstruction: A Baseline Show Chinese title

Title: 反中子重建的视觉量热器：基准

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. Show Chinese abstract

Abstract: 在高能物理中，反中子（$\bar{n}$）是经常作为末态粒子出现的基本粒子，其动量特性的重建为理解支配原理提供了重要的探针。 然而，这在电磁量能器（EMC）方面遇到了重大的技术挑战，EMC是一种典型的实验传感器，但对入射$\bar{n}$的信息恢复不足。 在本研究中，我们引入了视觉量能器（ViC），这是一种用于反中子重建的基准方法，利用深度学习探测器分析EMC响应与入射$\bar{n}$特征之间的隐含关系。 我们的动机在于，$\bar{n}$样本在EMC单元阵列中沉积的能量分布体现了丰富的上下文信息。 转换为二维图像后，这种上下文能量分布可以通过深度学习探测器以及伪边界框和指定的训练目标来预测$\bar{n}$（$i.e.$，入射位置和动量）的状态。 实验结果表明，ViC显著优于传统的重建方法，将入射位置的预测误差降低了42.81%（从17.31$^{\circ}$降至9.90$^{\circ}$）。更重要的是，这项研究首次实现了入射$\bar{n}$动量的测量，突显了深度学习探测器在粒子重建中的潜力。代码可在 https://github.com/yuhongtian17/ViC 获取。