Published August 26, 2023 | Version v1

L2LFlows: Generating High-Fidelity 3D Calorimeter Images

  • 1. Universität Hamburg, University of Geneva, HES-SO Geneva
  • 2. Rutgers University, Universität Heidelberg
  • 3. Universität Hamburg
  • 4. Rutgers University
  • 5. Universität Hamburg, CDCS (Hamburg)
  • 6. DESY (Hamburg)
  • 7. DESY (Hamburg), CDCS (Hamburg)

Description

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: 

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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

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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: 

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import torch

list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") 

[showers_geant, inc_energies_geant] = list_tensors

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A detailed description of how the datasets were simulated can be found in the paper. 

Files

l2lflows_geant4_datasets.zip

Files (7.6 GB)

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md5:8d37842d1152d426b763d276251c5f82
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Additional details

Related works

Is described by
Preprint: arXiv:2302.11594v1 (arXiv)