Euclid preparation. XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned
Euclid Collaboration et al.
Fecha de publicación:
1
2022