001     619033
005     20250527210440.0
024 7 _ |a Blekman:2024wyf
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024 7 _ |a inspire:2797728
|2 inspire
024 7 _ |a arXiv:2406.08590
|2 arXiv
024 7 _ |a 10.3204/PUBDB-2024-07333
|2 datacite_doi
037 _ _ |a PUBDB-2024-07333
041 _ _ |a English
082 _ _ |a 530
088 _ _ |a arXiv:2406.08590
|2 arXiv
088 _ _ |a DESY-24-086
|2 DESY
100 1 _ |a Blekman, Freya
|0 P:(DE-H253)PIP1097620
|b 0
|u desy
245 _ _ |a Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
260 _ _ |c 2024
336 7 _ |a Preprint
|b preprint
|m preprint
|0 PUB:(DE-HGF)25
|s 1748344278_497791
|2 PUB:(DE-HGF)
336 7 _ |a WORKING_PAPER
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336 7 _ |a Electronic Article
|0 28
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336 7 _ |a preprint
|2 DRIVER
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a Output Types/Working Paper
|2 DataCite
500 _ _ |a Version submitted to the European Physical Journal C
520 _ _ |a 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.
536 _ _ |a 611 - Fundamental Particles and Forces (POF4-611)
|0 G:(DE-HGF)POF4-611
|c POF4-611
|f POF IV
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536 _ _ |a DFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe (390833306)
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588 _ _ |a Dataset connected to INSPIRE
693 _ _ |0 EXP:(DE-H253)FCC-20190101
|5 EXP:(DE-H253)FCC-20190101
|e Future Circular Collider
|x 0
700 1 _ |a Canelli, Florencia
|b 1
700 1 _ |a De Moor, Alexandre
|b 2
700 1 _ |a Gautam, Kunal
|0 P:(DE-H253)PIP1104458
|b 3
|e Corresponding author
700 1 _ |a Ilg, Armin
|b 4
700 1 _ |a Macchiolo, Anna
|b 5
700 1 _ |a Ploerer, Eduardo
|b 6
856 4 _ |y OpenAccess
|u https://bib-pubdb1.desy.de/record/619033/files/2406.08590v4.pdf
856 4 _ |y OpenAccess
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909 C O |o oai:bib-pubdb1.desy.de:619033
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910 1 _ |a Deutsches Elektronen-Synchrotron
|0 I:(DE-588b)2008985-5
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|6 P:(DE-H253)PIP1097620
910 1 _ |a External Institute
|0 I:(DE-HGF)0
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910 1 _ |a External Institute
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913 1 _ |a DE-HGF
|b Forschungsbereich Materie
|l Matter and the Universe
|1 G:(DE-HGF)POF4-610
|0 G:(DE-HGF)POF4-611
|3 G:(DE-HGF)POF4
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|v Fundamental Particles and Forces
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914 1 _ |y 2024
915 _ _ |a OpenAccess
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915 _ _ |a Published
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915 _ _ |a Creative Commons Attribution-NonCommercial-ShareAlike CC BY-NC-SA 3.0
|0 LIC:(DE-HGF)CCBYNCSA3
|2 HGFVOC
920 1 _ |0 I:(DE-H253)CMS-20120731
|k CMS
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|x 0
980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-H253)CMS-20120731
980 1 _ |a FullTexts


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