TY  - EJOUR
AU  - Blekman, Freya
AU  - Canelli, Florencia
AU  - De Moor, Alexandre
AU  - Gautam, Kunal
AU  - Ilg, Armin
AU  - Macchiolo, Anna
AU  - Ploerer, Eduardo
TI  - Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
IS  - arXiv:2406.08590
M1  - PUBDB-2024-07333
M1  - arXiv:2406.08590
M1  - DESY-24-086
PY  - 2024
N1  - Version submitted to the European Physical Journal C
AB  - 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<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.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.3204/PUBDB-2024-07333
UR  - https://bib-pubdb1.desy.de/record/619033
ER  -