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

• URL: http://arxiv.org/abs/2408.10599v2
• Date: Sun, 16 Feb 2025 14:59:08 GMT
• Title: Vision Calorimeter: Migrating Visual Object Detector to High-energy Particle Images
• Authors: Hongtian Yu, Yangu Li, Yunfan Liu, Yunxuan Song, Xiaorui Lyu, Qixiang Ye,
• Abstract summary: Vision Calorimeter (ViC) is a data-driven framework which migrates visual object detection techniques to high-energy particle images. ViC significantly outperforms traditional approaches, reducing the incident position prediction error by 46.16%. This study underscores ViC's great potential as a general-purpose particle parameter estimator in high-energy physics.
• Score: 32.42087197412159
• License:
• 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.

Related papers

• Interpreting Transformers for Jet Tagging [2.512200562089791]
  This study focuses on interpreting ParT by analyzing attention heat maps and particle-pair correlations on the $eta$-$phi$ plane. At the same time, we observe that ParT shows varying focus on important particles and subjets depending on decay, indicating that the model learns traditional jet substructure observables.
  arXiv  Detail & Related papers  (2024-12-04T19:06:40Z)
• Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models [0.0]
  Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the GlueX experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods.
  arXiv  Detail & Related papers  (2024-07-10T05:37:02Z)
• Thermalization and Criticality on an Analog-Digital Quantum Simulator [133.58336306417294]
  We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions. We digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization.
  arXiv  Detail & Related papers  (2024-05-27T17:40:39Z)
• vHeat: Building Vision Models upon Heat Conduction [63.00030330898876]
  vHeat is a novel vision backbone model that simultaneously achieves both high computational efficiency and global receptive field. The essential idea is to conceptualize image patches as heat sources and model the calculation of their correlations as the diffusion of thermal energy.
  arXiv  Detail & Related papers  (2024-05-26T12:58:04Z)
• TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection [58.00308680221481]
  Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. We propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction.
  arXiv  Detail & Related papers  (2024-02-03T05:51:22Z)
• Object-centric Cross-modal Feature Distillation for Event-based Object Detection [87.50272918262361]
  RGB detectors still outperform event-based detectors due to sparsity of the event data and missing visual details. We develop a novel knowledge distillation approach to shrink the performance gap between these two modalities. We show that object-centric distillation allows to significantly improve the performance of the event-based student object detector.
  arXiv  Detail & Related papers  (2023-11-09T16:33:08Z)
• Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions [2.5521723486759407]
  This paper presents a solution that leverages the power of deep learning to accurately extract individual objects within multi-dimensional overlapping-sparse images. It is a direct application in high-energy physics with decomposition of overlaid elementary particles obtained from imaging detectors.
  arXiv  Detail & Related papers  (2023-10-30T16:12:25Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence [100.6913091147422]
  Existing rotated object detectors are mostly inherited from the horizontal detection paradigm. In this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology.
  arXiv  Detail & Related papers  (2021-06-03T14:29:19Z)
• Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data [4.653747487703939]
  Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. The clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. This paper uses Graph Neural Networks (GNNs) to predict the adjacency matrix of EM shower fragments and to identify the origin of showers.
  arXiv  Detail & Related papers  (2020-07-02T18:32:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.