Vision Calorimeter for High-Energy Particle Detection

TL;DR

The paper introduces Vision Calorimeter (ViC), a novel framework that adapts visual object detection techniques with a physics-inspired operator to improve anti-neutron parameter estimation in high-energy physics.

Contribution

It presents a physics-inspired heat-conduction operator integrated into a visual detector framework, enabling more accurate particle image analysis and parameter prediction.

Findings

Reduces incident position prediction error by 46.16%.

Achieves a momentum regression error of 21.48%.

Outperforms conventional methods significantly.

Abstract

In high-energy physics, estimating anti-neutron parameters (position and momentum) using the electromagnetic calorimeter (EMC) is crucial but challenging. To conquer this challenge, we propose Vision Calorimeter (ViC), a framework that migrates visual object detectors to analyze particle images. The motivation lies in introducing a physics-inspired heat-conduction operator (HCO) into the detector's backbone and head to handle the discrete and sparse patterns of these images. Implemented via the Discrete Cosine Transform, HCO extracts frequency-domain features, bridging the distribution gap between natural and particle images. Experiments demonstrate that ViC significantly outperforms conventional methods, reducing the incident position prediction error by 46.16% (from 17.31{\deg} to 9.32{\deg}) and providing the first baseline result with an incident momentum regression error of 21.48%. This study underscores ViC's great potential as a reliable particle detector for high-energy physics. Code is available at https://github.com/yuhongtian17/ViC.