Document Type
Article
Department/Program
Data Science
Journal Title
Machine Learning: Science and Technology
Pub Date
11-2022
Publisher
IOP Publishing
Volume
3
Issue
4
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the 'flux' stage we learn the distribution of a reference class. The 'mutability' stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.
Recommended Citation
Fanelli, Cristiano; Giroux, James; and Papandreou, Z., 'Flux+Mutability': A Conditional Generative Approach to One-class Classification and Anomaly Detection (2022). Machine Learning: Science and Technology, 3(4).
https://doi.org/10.1088/2632-2153/ac9bcb
DOI
https://doi.org/10.1088/2632-2153/ac9bcb
