Orateur
Description
Upcoming surveys such as Euclid and Rubin will probe the matter distribution through weak lensing with unprecedented precision. However, reconstructing mass maps from noisy shear measurements is fundamentally an ill-posed inverse problem. While current deep-learning approaches achieve high reconstruction accuracy, they either must require retraining for each new sky region (due to varying mask and noise setting), or rely on slow MCMC sampling.
We introduce PnPMass, a plug-and-play method that alternates between a gradient step on a noise-whitened data-fidelity term, and a denoising step performed by a single neural network trained on simulations corrupted by Gaussian white noise. On simulated mass maps, it achieves accuracy close to the best deep-learning methods, converges in a few iterations, and requires no retraining when the mask and noise setting change. We then extend the method to tomographic mass mapping in order to resolve structures along the line of sight. Because the lensing signal is strongly correlated across redshift bins, we propose to apply a joint reconstruction by replacing the denoiser with a multichannel network. In preliminary tests on simulations, this approach, referred to as PnPMass3D, reduces the root-mean-square error by 20% over applying PnPMass to each bin independently. Furthermore, the gap is much larger once the mass estimates are disentangled across redshift bins with the Bernardeau-Nishimichi-Taruya (BNT) transform: independent reconstruction shows spurious foreground contamination in higher-redshift bins, whereas PnPMass3D recovers much more accurate per-bin mass estimates.
Link to the published paper: https://www.aanda.org/articles/aa/abs/2026/06/aa57652-25/aa57652-25.html. Note: the tomographic extension is not included; this is a work in progress.