TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysProc.18.002
TI  - Machine learning in top quark physics at ATLAS and CMS
PY  - 2026/01/29
UR  - https://scipost.org/SciPostPhysProc.18.002
JF  - SciPost Physics Proceedings
JA  - SciPost Phys. Proc.
VL  - 
IS  - 18
SP  - 002
A1  - Komm, Matthias
AB  - This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for the efficient reconstruction of recorded collision events and innovative methods for statistical inference. Recent applications of some techniques by the ATLAS and CMS collaborations are also highlighted.
ER  -

@Article{10.21468/SciPostPhysProc.18.002,
	title={{Machine learning in top quark physics at ATLAS and CMS}},
	author={Matthias Komm and on behalf of the ATLAS and CMS collaborations},
	journal={SciPost Phys. Proc.},
	pages={002},
	year={2026},
	publisher={SciPost},
	doi={10.21468/SciPostPhysProc.18.002},
	url={https://scipost.org/10.21468/SciPostPhysProc.18.002},
}