Gaussian processes on ray-guided transformed uniform grids for fast, flexible, and auto-differentiable adaptive source reconstruction in lens modelling

Clicks: 2
ID: 333475
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,262 of 1,279 articles by views in monthly notices of the royal astronomical society

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,279 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract Strong gravitational lensing constrains cosmology and dark matter, but robust inference requires accurate source reconstruction. The achievable source resolution is highly position-dependent. Adaptive meshes can place resolution where needed, but typically rely on discontinuous operations, such as Delaunay tessellations or Voronoi binning, which can restrict regularization choices and break differentiability. In this paper, we present a novel approach for modelling the source on a ray-guided transformed uniform grid (RTU grid), that is adaptive to the lens mass model, auto-differentiable and flexible with respect to the regularization by allowing for an arbitrary choice of power spectrum. We achieve this by defining the source as a Gaussian process on a uniform grid, which is then transformed based on the cumulative distributions of rays traced back to the source plane. This approach ensures that source pixels contain a more uniform number of rays. The approach is fast by leveraging the fast Fourier transform to describe the Gaussian process in Fourier space. We apply this new approach to mock data and show that it achieves comparable fit quality with fewer source pixels, typically corresponding to about a factor of two fewer pixels per dimension, and increases Evidence Lower Bounds (ELBOs) for the same number of pixels. Using the RTU grid only mildly affects the difference in ELBO for models with and without substructures within lens galaxies. A fast, flexible, and auto-differentiable source reconstruction can greatly benefit the analysis of large samples of lens systems, e.g. those found within the Euclid survey.
Reference Key
openalex_W7166712576 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wolfgang Enzi, Coleman Krawczyk, Tian Li, Thomas E. Collett
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag2008
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.