"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection

TL;DR

Flux+Mutability is a novel conditional generative model combining clustering for one-class classification and anomaly detection, demonstrated on physics data to identify anomalies with minimal assumptions and high flexibility.

Contribution

The paper introduces Flux+Mutability, a new architecture that integrates generative modeling with clustering for improved anomaly detection and one-class classification.

Findings

Effective detection of anomalous dijets events from QCD background.

Flexible architecture adaptable to various problems including multi-class classification.

Limits assumptions on reference samples and remains agnostic to the complementary class.

Abstract

Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is at the forefront of 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.