Learning new physics efficiently with nonparametric methods.

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.

Learning new physics from an imperfect machine.

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

Composite Heavy Vector Triplet in the ATLAS Diboson Excess.

Vector triplets of the standard model SU(2)_{L} group are a universal prediction of "natural" new physics models involving a new composite sector and are therefore among the most plausible new particles that the LHC could discover. We consider the possibility that one such triplet could account for the ATLAS excess in the boson-tagged jets analysis and we assess the compatibility of this hypothesis with all other relevant searches. We find that the hypothesis is not excluded and that the predicted signal is close to the expected sensitivity of several channels, some of which show an upper fluctuation of the observed limit while others do not. An accurate study of the signal compatibility with these fluctuations could only be performed by the experimental collaborations.