Published May 24, 2024 | Version v1
Dataset Open

Deep learning insights into non-universality in the halo mass function

Authors/Creators

  • 1. ROR icon University College London
  • 2. Max-Planck-Institut für Astrophysik
  • 3. ROR icon University of Cambridge
  • 4. ROR icon Stockholm University
  • 5. ROR icon University of Geneva

Description

Data used in paper Deep learning insights into non-universality in the halo mass function (DOI: doi.org/10.1093/mnras/stae1696).

Model_outputs.zip contains the saved latents, predictions, ground truths, and the relevant cosmological quantities used in the analysis (see Model_outputs_readme.md).

Trained_model.zip has the trained baseline IVE model. It contains the saved model weights and code needed to build and load the model weights, as well as the test dataset (see Trained_model_readme.md).

Files

Model_outputs.zip

Files (422.9 MB)

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Additional details

Related works

Is supplement to
Journal article: 10.1093/mnras/stae1696 (DOI)