Jet Image Tagging Using Deep Learning: An Ensemble Model

2025-08-09

AI Summary

Jet substructure classification in high-energy physics is critical for discovering new physics, yet its complex, high-dimensional nature limits the performance of conventional approaches. This paper proposes a novel jet classification framework based on image-like representation and deep ensemble learning: particle-flow data are encoded as two-dimensional histograms, and a dual-branch neural network ensemble is constructed to jointly support both binary (e.g., top quark vs. light quark) and multi-class (top/light/W/Z) discrimination tasks. By integrating complementary representational strengths across diverse architectures, the framework significantly enhances feature extraction capability and generalization. Evaluated on the JetNet benchmark, the ensemble achieves superior accuracy over individual baseline models—particularly on challenging, ambiguously classified jets—demonstrating robustness and improved physical interpretability. The approach establishes a scalable deep learning paradigm for high-precision hadronic jet analysis.

Technology Category

Machine Learning: Ensemble MethodsComputer Vision: Representation Learning for VisionSearch and Optimization: Learning to Search

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and pose a challenge for identification due to their complex, multidimensional structure. Traditional classification methods often fall short in capturing these intricacies, necessitating advanced machine learning approaches. In this paper, we employ two neural networks simultaneously as an ensemble to tag various jet types. We convert the jet data to two-dimensional histograms instead of representing them as points in a higher-dimensional space. Specifically, this ensemble approach, hereafter referred to as Ensemble Model, is used to tag jets into classes from the JetNet dataset, corresponding to: Top Quarks, Light Quarks (up or down), and W and Z bosons. For the jet classes mentioned above, we show that the Ensemble Model can be used for both binary and multi-categorical classification. This ensemble approach learns jet features by leveraging the strengths of each constituent network achieving superior performance compared to either individual network.

Problem

• Classify jet types in high-energy physics using deep learning
• Improve jet identification beyond traditional methods' limitations
• Leverage ensemble neural networks for superior classification performance

Innovation

• Ensemble model with two neural networks
• Convert jet data to 2D histograms
• Superior performance in jet classification