Whitney, JJ;
Liaudat, TI;
Price, MA;
Mars, M;
McEwen, JD;
(2025)
Generative modelling for mass-mapping with fast uncertainty quantification.
Monthly Notices of the Royal Astronomical Society
, 542
(3)
pp. 2464-2479.
10.1093/mnras/staf1356.
Preview |
Text
Whitney_staf1356.pdf Download (2MB) | Preview |
Abstract
Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-mapping – a challenging ill-posed inverse problem where one infers the convergence field from observed shear measurements. Upcoming stage IV surveys, such as those made by the Vera C. Rubin Observatory and Euclid satellite, will provide a greater quantity and precision of data for lensing analyses, necessitating high-fidelity mass-mapping methods that are computationally efficient and that also provide uncertainties for integration into downstream cosmological analyses. In this work we introduce a novel generative adversarial network (GAN) for mass-mapping, which we call the Mass-Mapping GAN (MMGAN), based on a regularized conditional GAN framework, which generates approximate posterior samples of the convergence field given shear data. We adopt Wasserstein GANs to improve training stability and apply regularization techniques to overcome mode collapse, issues that otherwise are particularly acute for conditional GANs. We train and validate our model on a mock data set modeled after the Cosmic Evolution Survey (COSMOS) before applying it to true COSMOS data. Our approach significantly outperforms the Kaiser–Squires technique and achieves similar reconstruction fidelity as alternative state-of-the-art deep learning approaches. Notably, while alternative approaches for generating samples from a learned posterior are slow (e.g. requiring ∼10 GPU min per posterior sample), MMGAN can produce a high-quality convergence sample in less than a second.
| Type: | Article |
|---|---|
| Title: | Generative modelling for mass-mapping with fast uncertainty quantification |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/staf1356 |
| Publisher version: | https://doi.org/10.1093/mnras/staf1356 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | gravitational lensing: weak, methods: data analysis, software: machine learning, dark matter |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215372 |
Archive Staff Only
 |
View Item |
