Instrumentation and Detectors2024-05-17v3Machine LearningHigh Energy Physics - ExperimentHigh Energy Physics - PhenomenologyData Analysis, Statistics and Probability
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CaloFlow to the photon and charged pion Geant4 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 Geant4. 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 Geant4 samples.
Cite
@article{arxiv.2210.14245,
title = {CaloFlow for CaloChallenge Dataset 1},
author = {Claudius Krause and Ian Pang and David Shih},
journal= {arXiv preprint arXiv:2210.14245},
year = {2024}
}