CNN on `Top': In Search of Scalable & Lightweight Image-based Jet Taggers

TL;DR

This paper introduces a lightweight, scalable CNN-based jet tagging method that uses EfficientNet architecture and global features to achieve competitive performance in top-quark jet classification with reduced computational cost.

Contribution

It presents a novel, computationally inexpensive CNN approach leveraging EfficientNet and global features for effective jet tagging, emphasizing scalability and efficiency.

Findings

Competitive performance in top-quark jet tagging

Global features improve accuracy and reduce network complexity

EfficientNet-based model is computationally inexpensive

Abstract

While Transformer-based and standard Graph Neural Networks (GNNs) have proven to be the best performers in classifying different types of jets, they require substantial computational power. We explore the scope of using a lightweight and scalable version of EfficientNet architecture, along with global features of the jet. The end product is computationally inexpensive but is capable of competitive performance. We showcase the efficacy of our network in tagging top-quark jets in a sea of other light quark and gluon jets. The work also sheds light on the importance of global features for both the accuracy and the apparent redundancy of the network's complexity.