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@ARTICLE{Blekman:607315,
      author       = {Blekman, Freya and Moor, De and Gautam, Kunal and Ploerer,
                      Eduardo and Ilg, Armin and Macchiolo, Anna and Canellli,
                      Florencia},
      title        = {{T}agging more quark jet flavours at {FCC}-ee at 91 {G}e{V}
                      with a transformer-based neural network},
      journal      = {The European physical journal / C},
      volume       = {85},
      number       = {2},
      issn         = {1434-6044},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {PUBDB-2024-01826, DESY-24-086. arXiv:2406.08590},
      pages        = {165},
      year         = {2024},
      abstract     = {Jet flavour tagging is crucial in experimental high-energy
                      physics. A tagging algorithm, $\texttt{DeepJetTransformer},$
                      is presented, which exploits a transformer-based neural
                      network that is substantially faster to train. The
                      $\texttt{DeepJetTransformer}$ 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.},
      keywords     = {jet, flavor (INSPIRE) / track data analysis, vertex
                      (INSPIRE) / vertex, secondary (INSPIRE) / quark, flavor
                      (INSPIRE) / network (INSPIRE) / efficiency (INSPIRE) /
                      FCC-ee (INSPIRE) / background (INSPIRE) / neural network
                      (INSPIRE) / CERN LHC Coll (INSPIRE)},
      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)16},
      eprint       = {2406.08590},
      howpublished = {arXiv:2406.08590},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2406.08590;\%\%$},
      pubmed       = {pmid:39935486},
      UT           = {WOS:001416920500001},
      doi          = {10.1140/epjc/s10052-025-13785-y},
      url          = {https://bib-pubdb1.desy.de/record/607315},
}