I presented my research during the Workshop on Advances in Learning-Based Image Restoration, organized by CNRS and the IASIS research group. The workshop took place at the Institut Henri-Poincaré in Paris, France.

Title

A Plug-and-Play Approach with Conformal Predictions for Weak Lensing Mass Mapping

Abstract

In this talk, I will present a plug-and-play (PnP) approach for estimating the distribution of dark matter from noisy shear measurements, in the context of weak gravitational lensing. The method aims to provide accurate estimates efficiently while eliminating the need to train a deep learning model for each observed region of the sky. Instead, the approach requires training a model just once on simulated convergence maps corrupted with a Gaussian white noise. Additionally, we propose to apply a distribution-free uncertainty quantification (UQ) method, namely, conformalized quantile regression (CQR), to this mass mapping framework. Using a calibration set also derived from simulations, CQR provides coverage guarantees independent of any specific prior data distribution. We benchmark our results against CQR applied to existing mass mapping approaches such as Kaiser-Squires, Wiener, MCALens, and DeepMass. Our results reveal that, while the miscoverage rate remains constant across methods, the choice of such method significantly impacts the size of the error bars.

PDF presentation available here.