4 research outputs found
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
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