B-Jet Tagging with Retentive Networks: A Novel Approach and Comparative Study

TL;DR

This paper introduces Retentive Networks for b-jet tagging in collider experiments, demonstrating competitive performance with fewer parameters compared to existing models using simulated data.

Contribution

It presents a novel Retentive Network approach for b-jet tagging, showing its effectiveness and efficiency relative to state-of-the-art models.

Findings

Retentive Networks achieve promising b-jet tagging performance.

RetNet models use significantly fewer parameters than competitors.

RetNet is suitable for resource-limited computational environments.

Abstract

Identifying jets originating from bottom quarks is vital in collider experiments for new physics searches. This paper proposes a novel approach based on Retentive Networks (RetNet) for b-jet tagging using low-level features of jet constituents along with high-level jet features. A simulated \ttbar dataset provided by CERN CMS Open Data Portal was used, where only semileptonic decays of \ttbar pairs produced by 13 TeV proton-proton collisions are included. The performance of the newly proposed Retentive Network model is compared with state-of-the-art models such as DeepJet and Particle Transformer, as well as with a baseline MLP (Multi-Layer-Perceptron) classifier. Despite using a relatively smaller dataset, the Retentive Networks demonstrate a promising performance with only 330k trainable parameters. Results suggest that RetNet-based models can be used as an efficient alternative for b-jet with limited computational resources.