RanBox: anomaly detection in the copula space

• Published in: The journal of high energy physics Vol. 2023; no. 1; pp. 8 - 46
• Main Authors: Dorigo, Tommaso, Fumanelli, Martina, Maccani, Chiara, Mojsovska, Marija, Strong, Giles C., Scarpa, Bruno
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 03.01.2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Anomalies; Anomaly detection; Classical and Quantum Gravitation; Elementary Particles; Hadron-Hadron Scattering; High energy physics; Industrial applications; Particle collisions; Particle physics; Physics; Physics and Astronomy; Population density; Quantum Field Theories; Quantum Field Theory; Quantum Physics; Regular Article - Experimental Physics; Relativity Theory; String Theory; Subspaces; Unsupervised learning; Hadron-Hadron Scattering; Anomaly detection; Unsupervised learning
• ISSN: 1029-8479; 1029-8479
• DOI: 10.1007/JHEP01(2023)008

A bstract The unsupervised search for overdense regions in high-dimensional feature spaces, where locally high population densities may be associated with anomalous contaminations to an otherwise more uniform population, is of relevance to applications ranging from fundamental research to industrial use cases. Motivated by the specific needs of searches for new phenomena in particle collisions, we propose a novel approach that targets signals of interest populating compact regions of the feature space. The method consists in a systematic scan of subspaces of a standardized copula of the feature space, where the minimum p -value of a hypothesis test of local uniformity is sought by greedy descent. We characterize the performance of the proposed algorithm and show its effectiveness in several experimental situations.