Null Hypothesis Test for Anomaly Detection

Abstract

We extend the use of Classification Without Labels for anomaly detection with a hypothesis test designed to exclude the background-only hypothesis. By testing for statistical independence of the two discriminating dataset regions, we are able to exclude the background-only hypothesis without relying on fixed anomaly score cuts or extrapolations of background estimates between regions. The method relies on the assumption of conditional independence of anomaly score features and dataset regions, which can be ensured using existing decorrelation techniques. As a benchmark example, we consider the LHC Olympics dataset where we show that mutual information represents a suitable test for statistical independence and our method exhibits excellent and robust performance at different signal fractions even in presence of realistic feature correlations.

arXiv comment10 pages, 3 figures, 1 Table. Matches published version at Physics Letters B. All code is available at https://github.com/ManuelSzewc/Null_Hypothesis_Test_for_Anomaly_Detection. Comments welcome!

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• Adam: A Method for Stochastic Optimization
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• Anomaly Detection for Resonant New Physics with Machine Learning
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
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