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CaloFlow for CaloChallenge Dataset 1

by Claudius Krause, Ian Pang, David Shih

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Submission summary

Authors (as registered SciPost users): Claudius Krause
Submission information
Preprint Link: scipost_202404_00015v1  (pdf)
Date accepted: 2024-05-07
Date submitted: 2024-04-10 22:08
Submitted by: Krause, Claudius
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approach: Computational


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.

Author comments upon resubmission

Dear editor, dear referees,

please find our revised manuscript attached. As we detail in the response to the two referee's we still think that our submission contains enough new content to warrant a publication in SciPost Physics.

Best wishes,
Claudius Krause for the authors

List of changes

See the detailed replies to each referee.

Published as SciPost Phys. 16, 126 (2024)

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