Abstract
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.# Summary. An optional shortened abstract.
Marco Zanetti
Associated Professor
PhD at Univeristy of Padova, then research fellow at CERN and research associate at MIT. Since 2002 member of the CMS collaboration, spanning detector construction and commissioning, trigger and computing development and operations, Higgs discovery, searches for Dark Matter and New Physics. Now focused on advanced technologies and statistical methods to reduce bias in the analysis of complex datasets, in particular the developmet of a triggerless readout for HEP detectors and Machine Learning based anomaly detection algorithms.