DNN classifiers with mass-dependent thresholds reduce expected 95% CL upper limits on H to mu tau cross sections by 36-46% versus collinear mass baseline, while a regression network improves mass resolution by up to 21%.
Higgs to mu tau Decay in Supersymmetry without R Parity
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abstract
In this letter, we report on lepton flavor violating Higgs decay into mu+tau in the framework of the generic supersymmetric standard model without R parity and list interesting combinations of R-parity violating parameters. We impose other known experimental constraints on the parameters of the model and show our results from the R-parity violating parameters. In our analysis, the branching ratio of Higgs to mu+tau can exceed 10^{-5} within admissible parameter space.
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2026 1verdicts
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Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC
DNN classifiers with mass-dependent thresholds reduce expected 95% CL upper limits on H to mu tau cross sections by 36-46% versus collinear mass baseline, while a regression network improves mass resolution by up to 21%.