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Predicts 3D halo distributions from dark matter simulations using a physically motivated Wasserstein mapping network
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README.md
WGN_schematic.jpg
network_learning_movie.mp4
residual_inception_schematic.jpg
visual_comparison_N500_inset.jpg
wasserstein_halo_mapping_network.ipynb

README.md

halo_painting

Predicts 3D halo distributions from dark matter simulations using a physically motivated Wasserstein mapping network

The network architecture, training methodology and results are detailed in:

"Painting halos from 3D dark matter fields using Wasserstein mapping networks,"
Doogesh Kodi Ramanah, Tom Charnock, Guilhem Lavaux [arXiv:1903.10524]

Note:

  1. The notebook wasserstein_halo_painting_network.ipynb contains an in-depth and stepwise description of the network implementation and training;
  2. Please cite the above paper if you make use of our code;
  3. The network_learning_movie.mp4 depicts the network predictions for a given thick slice of dark matter density field, as a function of weight updates, and provides a visualization of the network learning progress.

Drawing

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