Triggering Dark Showers with Conditional Dual Auto-Encoders
Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda, Chernyavskaya, and Maurizio Pierini

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…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · COVID-19 diagnosis using AI · Anomaly Detection Techniques and Applications
MethodsAutoencoders
