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In this paper, we present several possible anomaly free implementations of the Branco-Grimus-Lavoura (BGL) model with two Higgs doublets and one singlet scalar. The model also includes three generations of massive neutrinos that get their mass via a type-I seesaw mechanism. A particular anomaly free realization, which we dub νBGL-1 scenario, is subjected to an extensive phenomenological analysis, from the perspective of flavor physics and collider phenomenology.

In this paper we study the use of machine learning techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the standard model effective field theory (SMEFT) framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the relation between the usual measures of performance in machine learning, such as area under the curve or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrizing systematic uncertainties. Our results show the potential of incorporating machine learning techniques to the SMEFT studies using the current datasets.