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We propose Multiscale Flow, a generative Normalizing Flow that creates
samples and models the field-level likelihood of two-dimensional cosmological
data such as weak lensing. Multiscale Flow uses hierarchical decomposition of
cosmological fields via a wavelet basis, and then models different wavelet
components separately as Normalizing Flows. The log-likelihood of the original
cosmological field can be recovered by summing over the log-likelihood of each
wavelet term. This decomposition allows us to separate the information from
different scales and identify distribution shifts in the data such as unknown
scale-dependent systematics. The resulting likelihood analysis can not only
identify these types of systematics, but can also be made optimal, in the sense
that the Multiscale Flow can learn the full likelihood at the field without any
dimensionality reduction. We apply Multiscale Flow to weak lensing mock
datasets for cosmological inference, and show that it significantly outperforms
traditional summary statistics such as power spectrum and peak counts, as well
as novel Machine Learning based summary statistics such as scattering transform
and convolutional neural networks. We further show that Multiscale Flow is able
to identify distribution shifts not in the training data such as baryonic
effects. Finally, we demonstrate that Multiscale Flow can be used to generate
realistic samples of weak lensing data.