Citation

Jahin, Md Abrar and Soudeep, Shahriar and Aditta, Arian Rahman and Mridha, M. F. and Fahad, Nafiz and Hossen, Md. Jakir (2025) Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images. In: 3rd International Workshop on Generalizing from Limited Resources in the Open World, GLOW 2025, Held in Conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2025, 16 August 2025 - 22 August 2025, Montreal, Canada.

Text 28.pdf - Published Version Restricted to Repository staff only Download (230kB)

Abstract

Distinguishing between quark- and gluon-initiated jets is a critical and challenging task in high-energy physics, pivotal for improving new physics searches and precision measurements at the Large Hadron Collider. While deep learning, particularly Convolutional Neural Networks (CNNs), has advanced jet tagging using image-based representations, the potential of Vision Transformer (ViT) architectures, renowned for modeling global contextual information, remains largely underexplored for direct calorimeter image analysis, especially under realistic detector and pileup conditions. This paper presents a systematic evaluation of ViTs and ViT-CNN hybrid models for quark-gluon jet classification using simulated 2012 CMS Open Data. We construct multi-channel jet-view images from detector-level energy deposits (ECAL, HCAL) and reconstructed tracks, enabling an end-to-end learning approach. Our comprehensive benchmarking demonstrates that ViT-based models, notably ViT+MaxViT and ViT+ConvNeXt hybrids, consistently outperform established CNN baselines in F1-score, ROC-AUC, and accuracy, highlighting the advantage of capturing long-range spatial correlations within jet substructure. This work establishes the first systematic framework and robust performance baselines for applying ViT architectures to calorimeter image-based jet classification using public collider data, alongside a structured dataset suitable for further deep learning research in this domain. The implementation of our code is available at: https://github.com/Abrar2652/particle_reconstruction/

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Particle reconstruction
Subjects:	Q Science > QA Mathematics > QA150-272.5 Algebra
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 09:18
Last Modified:	04 Oct 2025 09:49
URII:	http://shdl.mmu.edu.my/id/eprint/14628

Downloads

Edit (login required)