Secondary Vertex Finding in Jets with Neural Networks
Abstract
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.
Cite
@article{arxiv.2008.02831,
title = {Secondary Vertex Finding in Jets with Neural Networks},
author = {Jonathan Shlomi and Sanmay Ganguly and Eilam Gross and Kyle Cranmer and Yaron Lipman and Hadar Serviansky and Haggai Maron and Nimrod Segol},
journal= {arXiv preprint arXiv:2008.02831},
year = {2021}
}