Fermilab Theory Seminars

Null Hypothesis Test for Anomaly Detection

by Manuel Szewc (Cincinnati University)

Curia II

Curia II


In this talk we present a hypothesis test designed to exclude the background-only hypothesis for Anomaly detection searchs. Extending Classification Without Labels, we show that by testing for statistical independence of the two discriminating dataset regions, we are able 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.