Speaker
Prasanth Shyamsundar
(Fermi National Accelerator Laboratory)
Description
Recently a technique based on k-fold cross validation has become popular in anomaly detection in HEP, as a way to search for a wide class of anomalies in the data, without paying an associated penalty in sensitivity-depth. In this work, we point out that the breadth-depth tradeoff is an unavoidable aspect of anomaly detection, and cannot be overcome using the aforementioned k-fold cross adaptive search technique. Furthermore, we show that the technique leads to unaccounted-for look-elsewhere effect, i.e., the underestimation of p-values or the overestimation of significances of observed anomalies.
Primary authors
Nicholas Smith
(Fermi National Accelerator Laboratory)
Prasanth Shyamsundar
(Fermi National Accelerator Laboratory)
Manuel Szewc
(University of Cincinnati)
