%0 Journal Article
%A Blekman, Freya
%A Moor, De
%A Gautam, Kunal
%A Ploerer, Eduardo
%A Ilg, Armin
%A Macchiolo, Anna
%A Canellli, Florencia
%T Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
%J The European physical journal / C
%V 85
%N 2
%@ 1434-6044
%C Heidelberg
%I Springer
%M PUBDB-2024-01826
%M DESY-24-086
%M arXiv:2406.08590
%P 165
%D 2024
%X Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, <tt>DeepJetTransformer</tt>, is presented, which exploits a transformer-based neural network that is substantially faster to train. The <tt>DeepJetTransformer</tt> 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<sup>0</sup>s and K<sup>±</sup>/π<sup>±</sup> 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<sup>0</sup>s and K<sup>±</sup>/π<sup>±</sup> discrimination is presented. The network is applied on exclusive Z → q―q samples to examine the physics potential and is shown to isolate Z → s―s events. Assuming all other backgrounds can be efficiently rejected, a 5σ discovery significance for Z → s―s can be achieved with an integrated luminosity of 60 \textnb<sup>−1</sup>, corresponding to less than a second of the FCC-ee run plan at the Z resonance.
%K jet, flavor (INSPIRE)
%K track data analysis, vertex (INSPIRE)
%K vertex, secondary (INSPIRE)
%K quark, flavor (INSPIRE)
%K network (INSPIRE)
%K efficiency (INSPIRE)
%K FCC-ee (INSPIRE)
%K background (INSPIRE)
%K neural network (INSPIRE)
%K CERN LHC Coll (INSPIRE)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39935486
%U <Go to ISI:>//WOS:001416920500001
%R 10.1140/epjc/s10052-025-13785-y
%U https://bib-pubdb1.desy.de/record/607315