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%.
Search for lepton-flavour-violating $H\to\mu\tau$ decays of the Higgs boson with the ATLAS detector
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abstract
A direct search for lepton-flavour-violating (LFV) $H\to\mu\tau$ decays of the recently discovered Higgs boson with the ATLAS detector at the LHC is presented. The analysis is performed in the $H\to\mu\tau_{\mathrm{had}}$ channel, where $\tau_{\mathrm{had}}$ is a hadronically decaying $\tau$-lepton. The search is based on the data sample of proton--proton collisions collected by the ATLAS experiment corresponding to an integrated luminosity of 20.3 fb$^{-1}$ at a centre-of-mass energy of $\sqrt{s}=8$ TeV. No statistically significant excess of data over the predicted background is observed. The observed (expected) 95% confidence-level upper limit on the branching fraction, Br($H\to\mu\tau$), is 1.85% (1.24%).
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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%.