Abstract
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model. The main conceptual building blocks were introduced in D’Agnolo and Wulzer (Phys Rev D 99 (1), 015014. https://doi.org/10.1103/PhysRevD.99.015014. arXiv:1806.02350 [hep-ph], 2019). Here we make decisive progress in the algorithm implementation and we demonstrate its applicability to problems in high energy physics. We show that the method is sensitive to putative new physics signals in di-muon final states at the LHC. We also compare our performances on toy problems with the ones of alternative methods proposed in the literature.
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.