https://doi.org/10.1140/epjc/s10052-021-09342-y
Regular Article - Experimental Physics
Secondary vertex finding in jets with neural networks
1
Weizmann Institute of Science, Rehovot, Israel
2
NYU, New York, USA
3
NVIDIA Research, Tel Aviv, Israel
a
jonathan.shlomi@weizmann.ac.il
Received:
1
October
2020
Accepted:
14
June
2021
Published online:
23
June
2021
Jet classification is an important ingredient in measurements and searches for new physics at particle colliders, 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.
© The Author(s) 2021
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