Constraining neutrino masses with weak lensing multi-scale peak counts

Modeling the effect of massive neutrinos on the background evolution of the Universe and the growth of structure is one of the key challenges in modern cosmology. Weak-lensing cosmological constraints will also soon reach higher levels of precision with next-generation galaxy surveys like Euclid. To extract the non-Gaussian cosmological information encoded in cosmic shear data, weak lensing peak counts have proven to be a powerful tool. In this talk, I present the advantages of multi-scale filtering techniques when performing inference on cosmological parameters with peak counts computed on simulated weak lensing convergence maps as input data. To illustrate this, I will show the impact on cosmological constraints of a starlet filter and a multi-Gaussian filter applied on noisy convergence maps generated from the Cosmological Massive Neutrino Simulations (MassiveNuS) when employing the lensing power spectrum and peak counts as summary statistics.