Deep Learning for Observational Cosmology – DLOC


Dr. Tomasz Kacprzak (ETH Zürich)

Dr. Aurelien Lucchi (ETH Zürich)

Prof. Alexandre Refregier (ETH Zürich)

Prof. Thomas Hofmann (ETH Zürich)


Building on the latest developments in machine learning in order to address some major problems in cosmological analysis: 1. Observations are financially expensive 2. Simulations are computationally expensive


  1. Improve the classifier
  • Use convolutional neural networks
  1. Replace a part of the simulation with AI
  • Use generative adversarial networks


Cosmology science is heavily dependent on simulations and forward models. Algorithms, which can simulate cosmic structures had a large impact in and drove many branches of cosmology.