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Physics > Instrumentation and Detectors

Title: CaloFlow for CaloChallenge Dataset 1

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.
Comments: 36 pages, 21 figures, v3: match published version
Subjects: Instrumentation and Detectors (physics.ins-det); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph); Data Analysis, Statistics and Probability (physics.data-an)
Journal reference: SciPost Phys. 16, 126 (2024)
DOI: 10.21468/SciPostPhys.16.5.126
Cite as: arXiv:2210.14245 [physics.ins-det]
  (or arXiv:2210.14245v3 [physics.ins-det] for this version)

Submission history

From: Ian Pang [view email]
[v1] Tue, 25 Oct 2022 18:00:25 GMT (4897kb,D)
[v2] Mon, 7 Aug 2023 09:09:48 GMT (4156kb,D)
[v3] Wed, 15 May 2024 20:56:03 GMT (7825kb,D)

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