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%.
Lepton-flavor-violating Higgs decay $h \to \mu\tau$ and muon anomalous magnetic moment in a general two Higgs doublet model
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abstract
A two Higgs doublet model (2HDM) is one of the minimal extensions of the Standard Model (SM), and it is well-known that the general setup predicts the flavor-violating phenomena, mediated by neutral Higgs interactions. Recently the CMS collaboration has reported an excess of the lepton-flavor-violating Higgs decay in $h \rightarrow \mu \tau$ channel with a significance of 2.5 $ \sigma$. We investigate the CMS excess in a general 2HDM with tree-level Flavor Changing Neutral Currents (FCNCs), and discuss its impact on the other physical observations. Especially, we see that the FCNCs relevant to the excess can enhance the neutral Higgs contributions to the muon anomalous magnetic moment, and can resolve the discrepancy between the measured value and the SM prediction. We also find that the couplings to be consistent with the muon g-2 anomaly as well as the CMS excess in $h\rightarrow \mu\tau$ predict the sizable rate of $ \tau \rightarrow \mu \gamma$, which is within the reach of future B factory.
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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%.