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@ARTICLE{Blekman:619033,
author = {Blekman, Freya and Canelli, Florencia and De Moor,
Alexandre and Gautam, Kunal and Ilg, Armin and Macchiolo,
Anna and Ploerer, Eduardo},
title = {{T}agging more quark jet flavours at {FCC}-ee at 91 {G}e{V}
with a transformer-based neural network},
reportid = {PUBDB-2024-07333, arXiv:2406.08590. DESY-24-086},
year = {2024},
note = {Version submitted to the European Physical Journal C},
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. The
DeepJetTransformer network uses information from particle
flow-style objects and secondary vertex reconstruction as is
standard for $b$- and $c$-jet identification supplemented by
additional information, such as reconstructed V$^0$s and
$K^{\pm}/\pi^{\pm}$ discrimination, typically not included
in tagging algorithms at the LHC. The model is trained as a
multiclassifier to identify all quark flavours separately
and performs excellently in identifying $b$- and $c$-jets.
An $s$-tagging efficiency of $40\\%$ can be achieved with a
$10\\%$$ud$-jet background efficiency. The impact of
including V$^0$s and $K^{\pm}/\pi^{\pm}$ discrimination is
presented. The network is applied on exclusive $Z \to
q\bar{q}$ samples to examine the physics potential and is
shown to isolate $Z \to s\bar{s}$ events. Assuming all other
backgrounds can be efficiently rejected, a $5\sigma$
discovery significance for $Z \to s\bar{s}$ can be achieved
with an integrated luminosity of $60~\text{nb}^{-1}$,
corresponding to less than a second of the FCC-ee run plan
at the $Z$ resonance.},
cin = {CMS},
ddc = {530},
cid = {I:(DE-H253)CMS-20120731},
pnm = {611 - Fundamental Particles and Forces (POF4-611) / DFG
project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe
(390833306)},
pid = {G:(DE-HGF)POF4-611 / G:(GEPRIS)390833306},
experiment = {EXP:(DE-H253)FCC-20190101},
typ = {PUB:(DE-HGF)25},
eprint = {2406.08590},
howpublished = {arXiv:2406.08590},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2406.08590;\%\%$},
doi = {10.3204/PUBDB-2024-07333},
url = {https://bib-pubdb1.desy.de/record/619033},
}