Vision Calorimeter for High-Energy Particle Detection
Hongtian Yu, Yangu Li, Yunfan Liu, Yunxuan Song, Xiao-Rui Lyu, Qixiang Ye

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%.…
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Taxonomy
TopicsRadiation Detection and Scintillator Technologies · Nuclear Physics and Applications · Radiation Therapy and Dosimetry
