De-noising non-Gaussian fields in cosmology with normalizing flows by Adam Rouhiainen et al. on Monday 28 November

Fields in cosmology, such as the matter distribution, are observed by

experiments up to experimental noise. The first step in cosmological data

analysis is usually to de-noise the observed field using an analytic or

simulation driven prior. On large enough scales, such fields are Gaussian, and

the de-noising step is known as Wiener filtering. However, on smaller scales

probed by upcoming experiments, a Gaussian prior is substantially sub-optimal

because the true field distribution is very non-Gaussian. Using normalizing

flows, it is possible to learn the non-Gaussian prior from simulations (or from

more high-resolution observations), and use this knowledge to de-noise the data

more effectively. We show that we can train a flow to represent the matter

distribution of the universe, and evaluate how much signal-to-noise can be

gained as a function of the experimental noise under idealized conditions. We

also introduce a patching method to reconstruct fields on arbitrarily large

images by dividing them up into small maps (where we reconstruct non-Gaussian

features), and patching the small posterior maps together on large scales

(where the field is Gaussian).

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.15161v1