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Learning Multivariate New Physics
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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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Cited by 1 Pith paper
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Look everywhere effects in anomaly detection
Weakly supervised anomaly detectors that train and test on the same data produce badly miscalibrated p-values; independent test sets are calibrated but insensitive, while k-fold cross-validation is a workable middle ground.
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