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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.08134 |
| Etiquetas: |
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- 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.