Unfolding the matter distribution using 3-D weak gravitational lensing
Patrick Simon, Andy Taylor, Jan Hartlap

TL;DR
This paper presents a generalized, efficient method for 3-D matter density reconstruction from weak gravitational lensing data, enabling detailed large-scale structure mapping with realistic noise and bias considerations.
Contribution
It introduces a new minimum-variance estimator that combines redshift slices and broad distributions, improving 3-D matter mapping capabilities.
Findings
Feasible 3-D mass-density reconstruction on galaxy cluster scales.
Reconstruction S/N threshold of ~3 for structures above 10^14 Msol/h.
Potential for large-scale full-sky density field mapping.
Abstract
Combining redshift and galaxy shape information offers new exciting ways of exploiting the gravitational lensing effect for studying the large scales of the cosmos. One application is the three-dimensional reconstruction of the matter density distribution which is explored in this paper. We give a generalisation of an already known minimum-variance estimator of the 3-D matter density distribution that facilitates the combination of thin redshift slices of sources with samples of broad redshift distributions for an optimal reconstruction. We show how, in principle, intrinsic alignments of source ellipticities or shear/intrinsic alignment correlations can be accommodated, albeit these effects are not the focus of this paper. We describe an efficient and fast way to implement the estimator on a contemporary desktop computer. Analytic estimates for the noise and biases in the reconstruction…
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.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
