CaloFlow for CaloChallenge Dataset 1
This addresses the need for efficient simulation in particle physics experiments, though it appears incremental as it applies an existing method to a new dataset.
The paper tackled fast calorimeter simulation by applying CaloFlow, a normalizing flow approach, to photon and pion showers, achieving high-fidelity samples with sampling times orders of magnitude faster than Geant4.
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.