Deep Learning for Observational Cosmology – DLOC
Co-PIs:
- Dr. Tomasz Kacprzak (ETH Zürich)
- Dr. Aurelien Lucchi (ETH Zürich)
- Prof. Alexandre Refregier (ETH Zürich)
- Prof. Thomas Hofmann (ETH Zürich)
Project presentation
April 20th 2018
Problem:
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
Solution:
- Improve the classifier
- Use convolutional neural networks
- Replace a part of the simulation with AI
- Use generative adversarial networks
Impact:
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.
