Date: April 25th 2019, 11am
Speaker: Nial Jeffrey (UCL, currently visiting CosmoStat)
Title: Deep Learning dark matter maps from Dark Energy Survey (DES) weak lensing data
Room: Cassini
Abstract
Reconstructed density fields from weak lensing are rich in information about cosmological parameters and models of the Universe — including a large non-Gaussian component that cannot be accessed using traditional 2-point statistics. I will present a new method based on Deep Learning to reconstruct dark matter maps from weak lensing data with higher accuracy. Weak lensing map reconstruction is “ill-posed”, troubled by survey masks and galaxy “shape noise”. With DES SV data we showed that by implementing physically-motivated priors (Gaussian field or halo model), substantial improvements are made over standard approaches. Such advanced methods are still limited due to their prior distributions; non-linear density fields have no simple closed form that can be used as a prior. Deep Learning methods can directly learn the underlying structure of the signal, noise and mask from realistic simulations. By combining Deep Learning methods with a physically motivated closed-form prior, improved reconstruction is guaranteed.
