TY - JOUR
AU - Blekman, Freya
AU - Moor, De
AU - Gautam, Kunal
AU - Ploerer, Eduardo
AU - Ilg, Armin
AU - Macchiolo, Anna
AU - Canellli, Florencia
TI - Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
JO - The European physical journal / C
VL - 85
IS - 2
SN - 1434-6044
CY - Heidelberg
PB - Springer
M1 - PUBDB-2024-01826
M1 - DESY-24-086
M1 - arXiv:2406.08590
SP - 165
PY - 2024
AB - 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.
KW - jet, flavor (INSPIRE)
KW - track data analysis, vertex (INSPIRE)
KW - vertex, secondary (INSPIRE)
KW - quark, flavor (INSPIRE)
KW - network (INSPIRE)
KW - efficiency (INSPIRE)
KW - FCC-ee (INSPIRE)
KW - background (INSPIRE)
KW - neural network (INSPIRE)
KW - CERN LHC Coll (INSPIRE)
LB - PUB:(DE-HGF)16
C6 - pmid:39935486
UR - <Go to ISI:>//WOS:001416920500001
DO - DOI:10.1140/epjc/s10052-025-13785-y
UR - https://bib-pubdb1.desy.de/record/607315
ER -