TL;DR
This paper introduces DeepJetTransformer, a transformer-based neural network for quark jet flavour tagging at FCC-ee, which is faster to train and improves identification accuracy by incorporating additional particle information.
Contribution
The paper presents a novel transformer-based jet tagging algorithm that includes new particle identification features, enhancing flavour discrimination at FCC-ee.
Findings
Achieves 40% s-jet tagging efficiency with 10% ud-jet background.
Effectively isolates Z to s-sbar events.
Enables 5σ discovery of Z to s-sbar with minimal data.
Abstract
Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train than state-of-the-art graph neural networks. The DeepJetTransformer algorithm uses information from particle flow-style objects and secondary vertex reconstruction for - and -jet identification, supplemented by additional information that is not always included in tagging algorithms at the LHC, such as reconstructed and and discrimination. The model is trained as a multiclassifier to identify all quark flavours separately and performs excellently in identifying - and -jets. An -tagging efficiency of can be achieved with a -jet background efficiency. The performance improvement achieved by including…
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