Jet Image Tagging Using Deep Learning: An Ensemble Model

TL;DR

This paper introduces an ensemble deep learning approach that converts jet data into 2D histograms for improved classification of different jet types in high-energy physics, outperforming single models.

Contribution

It presents a novel ensemble neural network method using 2D histogram representations for jet classification, enhancing accuracy over individual models.

Findings

Ensemble model achieves higher accuracy than single networks.

Effective in classifying multiple jet types including top quarks and bosons.

Applicable to both binary and multi-class classification tasks.

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.