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CaloFlow for CaloChallenge dataset 1

Claudius Krause, Ian Pang, David Shih

SciPost Phys. 16, 126 (2024) · published 15 May 2024


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.

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