TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.16.5.126
TI  - CaloFlow for CaloChallenge dataset 1
PY  - 2024/05/15
UR  - https://scipost.org/SciPostPhys.16.5.126
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 16
IS  - 5
SP  - 126
A1  - Krause, Claudius
AU  - Pang, Ian
AU  - Shih, David
AB  - CALOFLOW is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CALOFLOW to the photon and charged pion ≥ant 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 ≥ant. 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 ≥ant samples.
ER  -

@Article{10.21468/SciPostPhys.16.5.126,
	title={{CaloFlow for CaloChallenge dataset 1}},
	author={Claudius Krause and Ian Pang and David Shih},
	journal={SciPost Phys.},
	volume={16},
	pages={126},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.16.5.126},
	url={https://scipost.org/10.21468/SciPostPhys.16.5.126},
}