Universal Anomaly Detection at the LHC: Transforming Optimal Classifiers and the DDD Method

TL;DR

This paper introduces the DDD method, transforming supervised classifiers into effective unsupervised anomaly detectors for LHC data, demonstrating its effectiveness across various scenarios and outperforming some existing methods.

Contribution

The paper presents the DDD method, a novel approach to convert supervised classifiers into unsupervised anomaly detectors, validated on LHC data and compared with existing techniques.

Findings

DDD is highly effective across various channels and signals.

Combining DeepSVDD and DDD enhances unsupervised anomaly detection.

Network architectures like particle transformers perform well as anomaly detectors.

Abstract

In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by training a discriminator model on both original and artificially modified datasets. We conducted a comprehensive evaluation of our models on the Dark Machines Anomaly Score Challenge channels and a search for 4-top quark events, demonstrating the effectiveness of our approach across various final states and beyond the Standard Model scenarios. We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well-established and well-performing method for anomaly detection. Results show that the effectiveness of each model varies by signal and channel, with DDD proving to be a very effective anomaly detector. We recommend the combined use of DeepSVDD and DDD for purely unsupervised applications, with the addition of flow models for improved performance when resources allow. Findings suggest that network architectures that excel in supervised contexts, such as the particle transformer with standard model interactions, also perform well as unsupervised anomaly detectors. We also show that with these methods, it is likely possible to recognize 4-top quark production as an anomaly without prior knowledge of the process. We argue that the Large Hadron Collider community can transform supervised classifiers into anomaly detectors to uncover potential new physical phenomena in each search.