Abstract Several improvements to the ATLAS triggers used to identify jets containing b -hadrons ( b -jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b -jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b -jet identification efficiency compared to the performance in Run 1 (2011–2012).more »
This content will become publicly available on June 1, 2022
Secondary vertex finding in jets with neural networks
Abstract 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.
- Award ID(s):
- 1836650
- Publication Date:
- NSF-PAR ID:
- 10256991
- Journal Name:
- The European Physical Journal C
- Volume:
- 81
- Issue:
- 6
- ISSN:
- 1434-6044
- Sponsoring Org:
- National Science Foundation
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