Machine Learning-Based b-Jet Tagging in pp Collisions at √(s)=13 TeV

2025/04/25 by Hadi Hassan, Hassan, Hadi, Neelkamal Mallick +3
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Nuclear Theory (nucl-th) #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions

paper · pdf · doi:10.48550/arxiv.2504.18291

openalex publication_date 2025/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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_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.

Citations

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Related

• Exploring an image-based b-jet tagging method using convolution neural networks
• Investigation of the performance of a GNN-based b-jet tagging method in heavy-ion collisions
• Machine Learning based jet momentum reconstruction in Pb-Pb collisions measured with the ALICE detector
• Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions
• Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment