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
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.