Triggering Dark Showers with Conditional Dual Auto-Encoders

• URL: http://arxiv.org/abs/2306.12955v1
• Date: Thu, 22 Jun 2023 15:13:18 GMT
• Title: Triggering Dark Showers with Conditional Dual Auto-Encoders
• Authors: Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, and Maurizio Pierini
• Abstract summary: Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders. We present a search formulated as an anomaly detection problem, using an AE to define a criterion to decide about the physics nature of an event. We propose a dual-encoder design which can learn a compact latent space through conditioning.
• Score: 1.2622086660704197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders, requiring little to no model-dependent assumptions. New hypothetical physics signals can be considered anomalies that deviate from the well-known background processes generally expected to describe the whole dataset. We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event. In this work, we perform an AD search for manifestations of a dark version of strong force using raw detector images, which are large and very sparse, without leveraging any physics-based pre-processing or assumption on the signals. We propose a dual-encoder design which can learn a compact latent space through conditioning. In the context of multiple AD metrics, we present a clear improvement over competitive baselines and prior approaches. It is the first time that an AE is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as a performant, model-independent algorithm to deploy, e.g., in the trigger stage of LHC experiments such as ATLAS and CMS.

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