Learning Multivariate New Physics
arXiv:1912.12155
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.
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- Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
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Cited by in corpus (6)
- Anomaly Detection with Density Estimation
- Simulation Assisted Likelihood-free Anomaly Detection
- A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
- Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
- E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
- A Quantum Algorithm for Model-Independent Searches for New Physics