https://doi.org/10.1140/epjp/s13360-024-05412-8
Regular Article
Machine-learning performance on Higgs-pair production associated with dark matter at the LHC
1
Departamento de Física Teórica and Instituto de Física Teórica UAM-CSIC, Universidad Autónoma de Madrid, Cantoblanco, 28049, Madrid, Spain
2
IFLP, CONICET - Dpto. de Física, Universidad Nacional de La Plata, C.C. 67, 1900, La Plata, Argentina
d
roberto.morales@fisica.unlp.edu.ar
Received:
13
May
2024
Accepted:
29
June
2024
Published online:
15
July
2024
Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to figure out the improvement capability of machine-learning tools over traditional cut-based analyses. In particular, boosted decision trees and neural networks are implemented in order to determine the parameter space that can be tested at the LHC demanding four b-jets and large missing energy in the final state. We present a performance comparison between both machine-learning algorithms, based on the maximum significance reached, by feeding them with different sets of kinematic features corresponding to the LHC at a center-of-mass energy of 14 TeV. Both algorithms present very similar performances and substantially improve traditional analyses, being sensitive to most of the parameter space considered for a total integrated luminosity of 1 ab
, with significances at the evidence level, and even at the discovery level, depending on the masses of the new heavy scalars. A more conservative approach with systematic uncertainties on the background of 30% has also been contemplated, again providing very promising significances.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
