CaloFlow for CaloChallenge dataset 1
Claudius Krause, Ian Pang, David Shih
Abstract
CALOFLOW is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CALOFLOW to the photon and charged pion ≥ant showers of Dataset 1 of the Fast Calorimeter Simulation Challenge 2022, we show how it can produce high-fidelity samples with a sampling time that is several orders of magnitude faster than ≥ant. We demonstrate the fidelity of the samples using calorimeter shower images, histograms of high level features, and aggregate metrics such as a classifier trained to distinguish CALOFLOW from ≥ant samples.
Topics & Concepts
Calorimeter (particle physics)FidelityHistogramComputer scienceHigh fidelitySampling (signal processing)Artificial intelligenceClassifier (UML)Pattern recognition (psychology)PhysicsDetectorImage (mathematics)AcousticsTelecommunicationsParticle physics theoretical and experimental studiesParticle Detector Development and PerformanceHigh-Energy Particle Collisions Research