Online-compatible unsupervised nonresonant anomaly detection
V. M. Mikuni, Benjamin Nachman, David Shih
Abstract
The authors of this paper employ two (or more) autoencoders to provide a complete strategy for unsupervised non-resonant anomaly detection. Both signal extraction and data-driven background estimation can be determined with decorrelated autoencoders. The method shows strong performance on test datasets and has the advantage of being online-compatible.
Topics & Concepts
Anomaly detectionComputer scienceSensitivity (control systems)Focus (optics)Context (archaeology)Anomaly (physics)SIGNAL (programming language)Artificial intelligencePattern recognition (psychology)Data miningMachine learningPhysicsEngineeringElectronic engineeringGeologyOpticsCondensed matter physicsPaleontologyProgramming languageAnomaly Detection Techniques and ApplicationsAstrophysics and Cosmic PhenomenaNeutrino Physics Research