Triggering Dark Showers with Conditional Dual Auto-Encoders

TL;DR

This paper introduces a novel conditional dual auto-encoder framework for model-independent anomaly detection of dark showers in collider data, demonstrating superior performance and potential for real-time physics event triggering.

Contribution

The paper proposes the first unsupervised dual auto-encoder model with spatial conditioning for detecting dark shower anomalies in collider data, improving over existing physics-based methods.

Findings

Outperforms traditional physics-based baselines in anomaly detection.

Effectively discriminates multiple dark shower models.

Suitable for real-time collider event triggering.

Abstract

We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g., in the real-time event triggering systems of Large Hadron Collider experiments such as ATLAS and CMS.