Secondary Vertex Finding in Jets with Neural Networks

Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the...

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Bibliographic Details
Published in:arXiv.org 2021-05
Main Authors: Shlomi, Jonathan, Ganguly, Sanmay, Gross, Eilam, Cranmer, Kyle, Lipman, Yaron, Hadar Serviansky, Maron, Haggai, Segol, Nimrod
Format: Article
Language:English
Subjects:
Classification
Neural networks
Performance measurement
Reconstruction
Online Access:Get full text
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Summary:Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance.
ISSN:2331-8422
DOI:10.48550/arxiv.2008.02831