Accelerating template generation in resonant anomaly detection searches with optimal transport

TL;DR

RAD-OT introduces an optimal transport-based method for rapid and stable template generation in resonant anomaly detection, effectively capturing complex relationships without relying on deep learning.

Contribution

The paper presents RAD-OT, a novel approach that uses optimal transport to efficiently generate signal templates, improving stability and performance over existing methods.

Findings

RAD-OT achieves comparable sensitivity to deep learning methods.

RAD-OT demonstrates improved stability in template generation.

RAD-OT efficiently captures multimodal relationships in data.

Abstract

We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the conditional probability density of the target features vary approximately linearly along the optimal transport path connecting the resonant feature. This does not assume that the conditional density itself is linear with the resonant feature, allowing RAD-OT to efficiently capture multimodal relationships, changes in resolution, etc. By solving the optimal transport problem, RAD-OT can quickly build a template by interpolating between the background distributions in two sideband regions. We demonstrate the performance of RAD-OT using the LHC Olympics R\&D dataset, where we find comparable sensitivity and improved stability with respect to deep learning-based approaches.