Machine Learning-Based b-Jet Tagging in pp Collisions at $\sqrt{s}=13$ TeV
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910918931120128 |
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| author | Hassan, Hadi Mallick, Neelkamal Kim, D. J. |
| author_facet | Hassan, Hadi Mallick, Neelkamal Kim, D. J. |
| contents | Studying heavy-flavor jets in pp collision is important since they can test pQCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the anti-$k_{\mathrm{T}}$ algorithm with a resolution parameter $R=$ 0.4 and with pseudorapidity $|η|<$ 0.5. Beauty jets are tagged using a machine learning model that uses a convolutional neural network trained on information extracted from the jet, tracks, and secondary vertices. The results show that this model is superior compared to other traditional tagging methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based b-Jet Tagging in pp Collisions at $\sqrt{s}=13$ TeV Hassan, Hadi Mallick, Neelkamal Kim, D. J. High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Theory Studying heavy-flavor jets in pp collision is important since they can test pQCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the anti-$k_{\mathrm{T}}$ algorithm with a resolution parameter $R=$ 0.4 and with pseudorapidity $|η|<$ 0.5. Beauty jets are tagged using a machine learning model that uses a convolutional neural network trained on information extracted from the jet, tracks, and secondary vertices. The results show that this model is superior compared to other traditional tagging methods. |
| title | Machine Learning-Based b-Jet Tagging in pp Collisions at $\sqrt{s}=13$ TeV |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2504.18291 |