Vision Calorimeter Achieves 46.16% Error Reduction in Particle Detection Accuracy

Researchers are tackling the difficult task of accurately measuring anti-neutron parameters in high-energy physics, a process vital for advancing our understanding of the universe. Hongtian Yu, Yangu Li, and Yunfan Liu, from the University of Chinese Academy of Sciences, alongside Yunxuan Song, Xiao-Rui Lyu, and Qixiang Ye et al., present a novel approach with their Vision Calorimeter (ViC) framework, which applies visual object detection techniques to particle image analysis. This research is significant because it introduces a physics-inspired heat-conduction operator to improve the detection of discrete and sparse patterns, achieving a substantial 46.16% reduction in incident position prediction error and establishing a new baseline for incident momentum regression with an error of 21.48%. ViC demonstrates considerable promise as a dependable particle detector for future high-energy physics experiments.

ViC framework estimates anti-neutron parameters via image analysis

Scientists have developed a novel framework called Vision Calorimeter (ViC) to significantly improve the estimation of anti-neutron parameters using electromagnetic calorimeters (EMCs), a crucial but challenging task in high-energy physics. The research team achieved this breakthrough by migrating visual object detectors, typically used for image analysis, to analyse particle images generated by EMCs. This innovative approach addresses the inherent difficulties in interpreting the discrete and sparse patterns present in these images, which traditionally hinder accurate parameter estimation of anti-neutrons, including incident position and momentum. ViC introduces a physics-inspired heat-conduction operator (HCO) integrated into both the backbone and head of the detector, enabling it to effectively process and interpret the unique characteristics of particle images.

The core of this work lies in the implementation of the HCO via the Discrete Cosine Transform, which extracts frequency-domain features and bridges the distribution gap between natural images and the distinct patterns found in particle physics data. By mapping the spatial arrangement of EMC cells onto image pixels and encoding recordings as RGB values, the researchers constructed high-energy particle images, effectively transforming the problem into a visual object detection task. A key adaptation involved modifying the HCO to make its heat conductivity coefficient dependent on the input samples, enhancing its ability to capture relevant features from the sparse data. This allows the framework to better align with pre-trained visual representations, facilitating transfer learning from natural image processing to the realm of high-energy physics.

Experiments demonstrate that ViC substantially outperforms conventional methods, reducing the incident position prediction error by 46.16%, from 17.31° to 9.32°. Importantly, this study provides the first baseline result for incident momentum regression, achieving an error of 21.48%. The team also introduced new metrics, mean angular bias for position prediction and mean relative error for momentum regression, guided by established physics practices to rigorously evaluate the accuracy of particle detection. The framework’s success stems from a combination of the HCO’s ability to extract meaningful features, a tailored annotation strategy for generating pseudo bounding boxes, and a refined detector head incorporating radial prior and global attention mechanisms.

This research establishes ViC as a reliable particle detector with great potential for high-energy physics, offering a powerful tool for analysing the extensive data generated by high-energy colliders. The work opens new avenues for employing deep learning techniques to improve the accuracy of particle property estimation, ultimately contributing to a more precise understanding of fundamental particles and their interactions. Code for ViC is publicly available, facilitating further research and development in this exciting field.

EMC Image Analysis via Heat-Conduction Operator

Scientists developed Vision Calorimeter (ViC), a novel framework that adapts visual object detectors for analysing particle images from electromagnetic calorimeters (EMCs) to estimate anti-neutron parameters. Researchers established a direct correspondence between the spatial arrangement of EMC cells and image pixels, effectively mapping the EMC surface onto an image plane. They then quantified EMC recordings as RGB values, constructing high-energy particle images representing final-state particles. Preliminary examination of these images revealed discrete and sparse patterns, prompting the team to engineer a physics-inspired heat-conduction operator (HCO) based on 2-D Discrete Cosine Transform (DCT) as a core component of the backbone network.

The HCO module extracts frequency-domain features, addressing the challenges posed by the discrete and sparse patterns inherent in particle images and facilitating alignment with pre-trained visual representations. To further enhance compatibility with visual object detection pipelines, scientists devised an annotation strategy to generate pseudo bounding boxes for training. Recognising a potential conflict between local attention mechanisms and the scattered nature of deposited energy, the study pioneered improvements to the detector head structure, incorporating both a radial prior and global attention mechanisms. This innovative approach allows for more accurate momentum regression.

Experiments employed mean angular bias (mAB) for position prediction and mean relative error (mRE) for momentum regression, metrics guided by established physics practices to rigorously evaluate particle detection accuracy. The resulting ViC framework achieved a mean angular bias of 9.32° and a mean relative error of 21.48%, demonstrating a substantial 46.16% reduction in incident position prediction error compared to conventional clustering algorithms which previously yielded 17.31°. This work represents the first end-to-end deep learning baseline for anti-neutron detection using EMC data, and importantly, enables the measurement of incident momentum for the first time. The team’s methodology, integrating the HCO with a visual object detector, unlocks the potential for modelling the extensive data generated by high-energy colliders and offers a reliable particle detector for high-energy physics. Code implementing this approach is publicly available to facilitate further research and development.

ViC framework improves anti-neutron parameter estimation accuracy

Scientists have developed a novel framework, Calorimeter (ViC), to estimate anti-neutron parameters using electromagnetic calorimeters (EMCs), addressing a long-standing challenge in high-energy physics. The research team migrated visual object detectors to analyse particle images, introducing a physics-inspired heat-conduction operator (HCO) into the detector’s architecture to effectively process the discrete and sparse patterns characteristic of these images. Implementing the HCO via the Discrete Cosine Transform, the team extracted frequency-domain features, bridging the gap between natural and particle images and enabling efficient transfer learning. Experiments revealed a significant reduction in incident position prediction error, decreasing from 17.31° to 9.32°, representing a 46.16% improvement over conventional methods.

This substantial enhancement demonstrates the effectiveness of ViC in precisely locating incident particles. Furthermore, the study delivered the first baseline result for incident momentum regression, recording an error of 21.48%. Measurements confirm that ViC not only improves position accuracy but also enables the measurement of a previously inaccessible parameter, incident momentum. The HCO, combining a radial prior with global attention, effectively captures the unique characteristics of particle patterns, ensuring seamless alignment with pre-trained visual representations. This innovative operator allows the framework to leverage knowledge gained from natural images, accelerating the learning process and improving performance on particle image analysis.

The team formatted high-energy particle images by arranging EMC cell arrays in a spherical coordinate system, projecting energy depositions into a 2-D image domain for subsequent analysis. Tests prove that ViC’s decoupled representation capability, inherited from object detectors, allows simultaneous incident position prediction and particle classification, crucial for anti-neutron parameter estimation. The framework’s ability to handle the imbalance between signal and background events further enhances its reliability in modelling the extensive data generated by high-energy colliders. Data shows that the approach offers a promising pathway towards more accurate and efficient particle detection in future high-energy physics experiments.

Vision Calorimeter improves anti-neutron detection accuracy significantly

Scientists have developed a novel deep learning approach called Vision Calorimeter (ViC) for detecting anti-neutrons using data from electromagnetic calorimeters. This framework adapts visual object detection techniques to analyse particle images, addressing the challenges posed by the discrete and sparse nature of these images. A key innovation is the incorporation of a physics-inspired heat-conduction operator (HCO) into the detector’s architecture, which extracts frequency-domain features and bridges the gap between natural and particle images. Experiments demonstrate that ViC significantly improves performance compared to conventional methods, reducing incident position prediction error by 46.16% (from 17.31° to 9.32°).

Notably, this study establishes the first baseline result for incident momentum regression, achieving an error of 21.48%. The research highlights the potential of adapting advanced machine learning to high-energy physics, revealing its effectiveness in a domain traditionally reliant on different analytical techniques. The authors acknowledge that the current implementation of ViC, tested within the BESIII experiment, is limited to particles with momenta below 1.2 GeV/c and benefits from a clean experimental environment without pile-up. Future work will focus on extending the approach to data from other high-energy physics experiments, contingent on data access policies. Furthermore, investigations into optimal pseudo bounding box sizes suggest that smaller boxes are better for position prediction, while larger ones are more suitable for momentum regression, offering a pathway for task-specific optimisation.