mlsanityData to 14 Oct 2026

Triggering Dark Showers with Conditional Dual Auto-Encoders

arXiv 2306.12955 ↗First posted 22 Jun 2023hep-ex cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 40 of its 96 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

40 of 96 references.

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30