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| 005 | 20250715151506.0 | ||
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| 024 | 7 | _ | |a 10.1140/epjc/s10052-025-13785-y |2 doi |
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| 088 | _ | _ | |a arXiv:2406.08590 |2 arXiv |
| 100 | 1 | _ | |a Blekman, Freya |0 P:(DE-H253)PIP1097620 |b 0 |
| 245 | _ | _ | |a Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network |
| 260 | _ | _ | |a Heidelberg |c 2024 |b Springer |
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| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a quark, flavor |2 INSPIRE |
| 650 | _ | 7 | |a network |2 INSPIRE |
| 650 | _ | 7 | |a efficiency |2 INSPIRE |
| 650 | _ | 7 | |a FCC-ee |2 INSPIRE |
| 650 | _ | 7 | |a background |2 INSPIRE |
| 650 | _ | 7 | |a neural network |2 INSPIRE |
| 650 | _ | 7 | |a CERN LHC Coll |2 INSPIRE |
| 693 | _ | _ | |0 EXP:(DE-H253)FCC-20190101 |5 EXP:(DE-H253)FCC-20190101 |e Future Circular Collider |x 0 |
| 700 | 1 | _ | |a Moor, De |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Gautam, Kunal |0 P:(DE-H253)PIP1104458 |b 2 |e Corresponding author |
| 700 | 1 | _ | |a Ploerer, Eduardo |0 P:(DE-H253)PIP1104457 |b 3 |
| 700 | 1 | _ | |a Ilg, Armin |0 P:(DE-H253)PIP1108470 |b 4 |
| 700 | 1 | _ | |a Macchiolo, Anna |0 P:(DE-H253)PIP1025156 |b 5 |
| 700 | 1 | _ | |a Canellli, Florencia |0 P:(DE-HGF)0 |b 6 |
| 773 | _ | _ | |a 10.1140/epjc/s10052-025-13785-y |g Vol. 85, no. 2, p. 165 |0 PERI:(DE-600)1459069-4 |n 2 |p 165 |t The European physical journal / C |v 85 |y 2024 |x 1434-6044 |
| 787 | 0 | _ | |a Blekman, Freya et.al. |d 2024 |i IsParent |0 PUBDB-2024-07333 |r arXiv:2406.08590 ; DESY-24-086 |t Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer |
| 856 | 4 | _ | |u https://arxiv.org/abs/2406.08590 |
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