"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection
This addresses anomaly detection for experimental physics, such as at the Large Hadron Collider, to find new physics beyond the Standard Model, but it is incremental as it builds on existing generative and clustering methods.
The paper tackles anomaly detection in experimental physics by introducing the Flux+Mutability architecture, which combines conditional generative models with clustering to learn reference distributions and detect deviations, demonstrating performance in isolating neutral showers and detecting anomalous dijets from QCD background.
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.