Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC
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10052_2025_Article_14713.pdf
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ceb2d146c91bfda173991116b393f7cc
Author(s)
CMS Collaboration
Date Issued
November 26, 2025
Journal
The European Physical Journal C
Publisher
Springer Berlin Heidelberg
Citation
CMS Collaboration. Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC. Eur. Phys. J. C 85, 1360 (2025).
Version
Final published version
Abstract
We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the τ τ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjc/s10052-025-14713-w
