Speaker
Description
Understanding the substructure of jets initiated by heavy quarks is essential for quantum chromodynamics (QCD) studies, particularly in the context of the dead-cone effect and jet quenching. The kinematics of b-hadron decays present a challenge for substructure measurements with inclusive b-jets. We propose an approach using geometric deep learning to extract the optimal representation of the b-hadron decays utilizing the charged decay products of the jet represented as a point cloud and identify tracks associated with the b-hadrons while simultaneously tagging the b-jets. The method is demonstrated in simulations of p-p and Pb-Pb collisions at $\sqrt{s} = 5.02$ TeV with the CMS detector and compared with previous approaches based on boosted decision trees.
| AI keywords | geometric deep learning; point cloud identification; signal reconstruction |
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