TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysProc.18.011
TI  - Transformer neural networks in the measurement of $t\bar{t}H$ production in the $H\,{\to}\,b\bar{b}$ decay channel with ATLAS
PY  - 2026/01/29
UR  - https://scipost.org/SciPostPhysProc.18.011
JF  - SciPost Physics Proceedings
JA  - SciPost Phys. Proc.
VL  - 
IS  - 18
SP  - 011
A1  - Scheulen, Chris
AB  - A measurement of Higgs boson production in association with a top quark pair in the bottom--anti-bottom Higgs boson decay channel and leptonic top 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. As a result, the observed (expected) event excess over the background-only hypothesis corresponds with a significance of 4.6 (5.4) standard deviations.
ER  -

@Article{10.21468/SciPostPhysProc.18.011,
	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={Chris Scheulen and on behalf of the ATLAS collaboration},
	journal={SciPost Phys. Proc.},
	pages={011},
	year={2026},
	publisher={SciPost},
	doi={10.21468/SciPostPhysProc.18.011},
	url={https://scipost.org/10.21468/SciPostPhysProc.18.011},
}