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Dataset . 2023
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L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Authors: Shekhzadeh, Imahn; Krause, Claudius; Diefenbacher, Sascha; Shih, David; Kasieczka, Gregor; Eren, Engin; Gaede, Frank;

L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Abstract

This upload contains the datasets used in arXiv:2302.11594. The file g4-showers_950k_10x10_train_val_test.pt contains the 760k training, 95k validation and 95k test showers as well as their incident energies. It should be loaded as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - import torch list_tensors = torch.load(args.file_path) for (idx, tensor) in enumerate(list_tensors): [showers_train, showers_val, showers_test, inc_energies_train, inc_energies_val, inc_energies_test] = list_tensors - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The file g4-showers_665k_10x10_test.pt contains 665k additional showers that were used for the classifier scaling studies, in addition to the 95k test showers from the file g4-showers_950k_10x10_train_val_test.pt. It should be loaded as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - import torch list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") [showers_geant, inc_energies_geant] = list_tensors - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - A detailed description of how the datasets were simulated can be found in the paper.

Keywords

Deep Generative Modeling, High Energy Physics

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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