A new approach to observational cosmology using the scattering transform

Understand

Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology.

• In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring no training nor tuning.
• It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information.
• It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field.