Full Phase Space Resonant Anomaly Detection

TL;DR

This paper introduces a novel machine learning approach that leverages point cloud generative models to perform resonant anomaly detection across the full phase space in collider data, enhancing search sensitivity.

Contribution

It demonstrates the application of full phase space anomaly detection using advanced generative models, expanding beyond previous limitations to include all relevant particles.

Findings

Successful detection of R&D dataset signal at the LHC Olympics

Method extends anomaly detection to higher-dimensional phase space

Opens new avenues for comprehensive collider data analysis

Abstract

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R\&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection.