Vision Calorimeter: Migrating Visual Object Detector to High-energy Particle Images

Abstract: In high-energy physics, accurately estimating the kinematic parameters (position and momentum) of anti-neutrons ($\bar{n}$) is essential for exploring the fundamental governing principles. However, this process is particularly challenging when using an electromagnetic calorimeter (EMC) as the energy detector, due to their limited accuracy and efficiency in interacting with $\bar{n}$. To address this issue, we propose Vision Calorimeter (ViC), a data-driven framework which migrates visual object detection techniques to high-energy particle images. To accommodate the unique characteristics of particle images, we introduce the heat-conduction operator (HCO) into both the backbone and the head of the conventional object detector and conduct significant structural improvements. HCO enjoys the advantage of both radial prior and global attention, as it is inspired by physical heat conduction which naturally aligns with the pattern of particle incidence. Implemented via the Discrete Cosine Transform (DCT), HCO extracts frequency-domain features, bridging the distribution gap between the particle images and the natural images on which visual object detectors are pre-trained. Experimental results demonstrate that ViC significantly outperforms traditional approaches, reducing the incident position prediction error by 46.16% (from 17.31$^{\circ}$ to 9.32$^{\circ}$) and providing the first baseline result with an incident momentum regression error of 21.48%. This study underscores ViC's great potential as a general-purpose particle parameter estimator in high-energy physics. Code is available at https://github.com/yuhongtian17/ViC.