| ATLAS Note | |
| Report number | arXiv:2412.08387 ; ATL-PHYS-PROC-2024-116 |
| Title | Transformer Neural Networks in the Measurement of $t\bar{t}H$ Production in the $H\to{}b\bar{b}$ Decay Channel with ATLAS |
| Author(s) | Scheulen, Chris (Geneva U.) |
| Corporate Author(s) | The ATLAS collaboration |
| Collaboration | ATLAS Collaboration |
| Publication | 2024 |
| Imprint | 11 Dec 2024 |
| Number of pages | 5 |
| Note | 5 pages, 3 figures. Young Scientist Forum Talk at the 17th International Workshop on Top Quark Physics (Top2024), 22-27 September 2024. Submission to SciPost |
| In: | 17th International Workshop on Top Quark Physics, Saint Malo, France, 22 - 27 Sep 2024 |
| Subject category | Particle Physics - Experiment |
| Accelerator/Facility, Experiment | CERN LHC ; ATLAS |
| Free keywords | Top ; Higgs ; ttH ; ATLAS ; Run 2 ; Transformer ; MVA ; TOP ; HIGGS |
| Abstract | A measurement of Higgs boson production in association with a top quark pair in the bottom--anti-bottom Higgs boson decay channel and leptonic final states is presented. The analysis uses $140\,\mathrm{fb}^{-1}$ of $13\,\mathrm{TeV}$ proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider. A particular focus is placed on the role played by transformer neural networks in discriminating signal and background processes via multi-class discriminants and in reconstructing the Higgs boson transverse momentum. These powerful multi-variate analysis techniques significantly improve the analysis over a previous measurement using the same dataset. |
| Copyright/License | preprint: (License: CC BY 4.0) |
Corresponding record in: Inspire
记录创建於2024-12-11，最後更新在2024-12-14