FETA: Flow-Enhanced Transportation for Anomaly Detection

Abstract

Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data. The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region. To illustrate this approach, we use simulated collisions from the Large Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed background method has competitive sensitivity with other recent proposals and can therefore provide complementary information to improve future searches.

arXiv comment13 pages, 11 figures. minor updates, v2 (published version)

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• Normalizing Flows for High-Dimensional Detector Simulations
• Combining Resonant and Tail-based Anomaly Detection
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• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
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• Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Non-resonant Anomaly Detection with Background Extrapolation
• Reweighting simulated events using machine-learning techniques in the CMS experiment
• Unsupervised and lightly supervised learning in particle physics
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• Finding excesses in model parameter space
• Data-Driven High-Dimensional Statistical Inference with Generative Models
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Strong CWoLa: Binary Classification Without Background Simulation
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