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CaloFlow for CaloChallenge Dataset 1
by Claudius Krause, Ian Pang, David Shih
This Submission thread is now published as
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 |
| Specialties: |
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| Approach: | Computational |
Abstract
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
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)