Learning Multivariate New Physics
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 Ref. [1]. 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.
Keywords
Cite
@article{arxiv.1912.12155,
title = {Learning Multivariate New Physics},
author = {Raffaele Tito D'Agnolo and Gaia Grosso and Maurizio Pierini and Andrea Wulzer and Marco Zanetti},
journal= {arXiv preprint arXiv:1912.12155},
year = {2021}
}