2021/06/10 by T. Dorigo, Dorigo, Tommaso, Martina Fumanelli +9 · 1 citation
Chemistry · Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Spectroscopy and Chemometric Analyses #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2106.05747
openalex publication_date 2021/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 gradient descent. We characterize the performance of the proposed algorithm and show its effectiveness in several experimental situations.