TL;DR
This paper investigates how physically motivated regularization priors improve the reconstruction of lens potentials and sources in gravitational lensing, reducing biases and overfitting, and introduces a new framework for analyzing degeneracies.
Contribution
It introduces observationally motivated priors for source and potential reconstructions and provides a quantitative framework for degeneracy analysis in lens modeling.
Findings
Physically motivated priors lead to lower residuals and less overfitting.
Incorrect regularization can bias potential perturbation reconstructions.
New semi-linear inversion method quantifies source-potential degeneracies.
Abstract
Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regularization has traditionally been used to constrain the solutions where the data failed to do so, e.g. in unlensed parts of the source. In this exploratory work, we go beyond the usual choices of regularization and adopt observationally motivated priors for the source brightness. We also perform a similar comparison when reconstructing lens potential perturbations, which are assumed to be stationary, i.e. permeate the entire field of view. We find that physically motivated priors lead to lower residuals, avoid overfitting, and are decisively preferred within a Bayesian quantitative framework in all the examples considered. For the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
