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.
Highlights
In the study of the fundamental laws of Nature we face a number of open questions
We show that the method is sensitive to putative new physics signals in di-muon final states at the Large Hadron Collider (LHC)
In this work we show the power of these techniques in the context of model-independent new physics searches at the LHC, expanding the framework of Ref. [1]
Summary
In the study of the fundamental laws of Nature we face a number of open questions. In the past decades the field of particle physics has produced a set of potential answers that seemed inevitable in their simplicity. In this paper we show how to interrogate experimental data in a new way, going beyond searches targeted at one specific theoretical model
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