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arXiv · 2504.18291

Machine learning-based b-jet tagging in $pp$ collisions at $\sqrt{s}=13$ TeV

Hadi Hassan·Neelkamal Mallick·D. J. Kim
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Abstract

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 $|\eta|<$ 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.

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Hadi Hassan, Neelkamal Mallick, D. J. Kim. 2025-04-25. Machine learning-based b-jet tagging in $pp$ collisions at $\sqrt{s}=13$ TeV. https://doi.org/10.1103/rw87-lyw8

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