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

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.