Euclid: Reconstruction of Weak Lensing mass maps for non-Gaussianity studies
S. Pires, V. Vandenbussche, V. Kansal, R. Bender, D. Bonino, A., Boucaud, J. Brinchmann, V. Capobianco, J. Carretero, M. Castellano, S., Cavuoti, R. Cl\'edassou, G. Congedo, L. Conversi, L. Corcione, F. Dubath, M., Frailis, E. Franceschi, M. Fumana, F. Grupp, F. Hormuth

TL;DR
This paper introduces a new mass-inversion method, KS+, for weak lensing convergence maps in Euclid, improving the accuracy of non-Gaussianity studies by reducing information loss and correcting systematic effects.
Contribution
The paper presents the KS+ mass-inversion method, an enhancement over the standard Kaiser & Squires approach, tailored for Euclid's full-sky weak lensing surveys.
Findings
KS+ reduces systematic effects in mass maps
Improved reconstruction quality over KS method
Better estimation of higher-order moments
Abstract
Weak lensing, which is the deflection of light by matter along the line of sight, has proven to be an efficient method for constraining models of structure formation and reveal the nature of dark energy. So far, most weak-lensing studies have focused on the shear field that can be measured directly from the ellipticity of background galaxies. However, within the context of forthcoming full-sky weak-lensing surveys such as Euclid, convergence maps (mass maps) offer an important advantage over shear fields in terms of cosmological exploitation. While it carry the same information, the lensing signal is more compressed in the convergence maps than in the shear field. This simplifies otherwise computationally expensive analyses, for instance, non-Gaussianity studies. However, the inversion of the non-local shear field requires accurate control of systematic effects caused by holes in 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.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
