Learnable wavelet neural networks for cosmological inference
Christian Pedersen Michael Eickenberg Shirley Ho
Convolutional neural networks (CNNs) have been shown to both extract more
information than the traditional two-point statistics from cosmological fields,
and marginalise over astrophysical effects extremely well. However, CNNs
require large amounts of training data, which is potentially problematic in the
domain of expensive cosmological simulations, and it is difficult to interpret
the network. In this work we apply the learnable scattering transform, a kind
of convolutional neural network that uses trainable wavelets as filters, to the
problem of cosmological inference and marginalisation over astrophysical
effects. We present two models based on the scattering transform, one
constructed for performance, and one constructed for interpretability, and
perform a comparison with a CNN. We find that scattering architectures are able
to outperform a CNN, significantly in the case of small training data samples.
Additionally we present a lightweight scattering network that is highly
interpretable.