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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.08134 |
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| _version_ | 1866912152082710528 |
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| author | Guvenli, Ayse Asu Isildak, Bora |
| author_facet | Guvenli, Ayse Asu Isildak, Bora |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | B-Jet Tagging with Retentive Networks: A Novel Approach and Comparative Study Guvenli, Ayse Asu Isildak, Bora High Energy Physics - Experiment 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. |
| title | B-Jet Tagging with Retentive Networks: A Novel Approach and Comparative Study |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2412.08134 |