Learning multivariate new physics

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 da...

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Bibliographic Details
Published in:The European physical journal. C, Particles and fields Particles and fields, 2021-01, Vol.81 (1), p.1-21, Article 89
Main Authors: D’Agnolo, Raffaele Tito, Grosso, Gaia, Pierini, Maurizio, Wulzer, Andrea, Zanetti, Marco
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Astronomy
Astrophysics and Cosmology
Data analysis
Datasets
Deviation
Elementary Particles
Hadrons
Heavy Ions
High Energy Physics - Experiment
High Energy Physics - Phenomenology
Information management
Measurement Science and Instrumentation
Multilayer perceptrons
Multivariate analysis
Nuclear Energy
Nuclear Physics
Physics
Physics and Astronomy
Quantum Field Theories
Quantum Field Theory
Regular Article – Experimental Physics
String Theory
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Summary: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.
ISSN:1434-6044
1434-6052
DOI:10.1140/epjc/s10052-021-08853-y