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%.
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In the 331ALP model the second Higgs is predicted above 600 GeV and the Z' above 5.1 TeV, with dark matter mass related to the axion breaking scale under a residual Z2 symmetry.
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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%.
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Heavy neutral bosons and dark matter in the 3-3-1 model with axionlike particle
In the 331ALP model the second Higgs is predicted above 600 GeV and the Z' above 5.1 TeV, with dark matter mass related to the axion breaking scale under a residual Z2 symmetry.