Learning new physics efficiently with nonparametric methods

Experimental observations and convincing conceptual arguments indicate that the present understanding of fundamental physics is not complete. Our theoretical formulation of the fundamental laws of Nature, the Standard Model, has been predicting with extremely high precision an impressive amount of data collected at past and ongoing experiments. On the other hand, the Standard Model does not provide answer to a multitude of questions including the origin of the electroweak scale, the mass of neutrinos, the flavour structure in the quark, lepton and neutrino sectors, and is unable to account for observed phenomena like the origin and the composition of the dark matter of the baryon asymmetry in the Universe. Further, it does not provide a microscopic description of gravity. These considerations guarantee the existence of more fundamental laws of Nature waiting to be unveiled. In order to access these laws, we must search the experimental data for phenomena that depart from the Standard Model predictions. Currently, the most common searching strategy is to test the data for the presence of specific new physics models, one at the time. Each search is then optimized to be sensitive to the features specific of the considered new physics scenario. This approach is in general insensitive to sources of discrepancy that differ from those considered.