ASKI: full-sky lensing map making algorithms
C. Pichon, E. Thiebaut, S. Prunet, K. Benabed, S. Colombi, T. Sousbie,, R. Teyssier

TL;DR
This paper introduces ASKI, an edge-preserving algorithm for full-sky lensing map making that effectively reconstructs convergence maps from masked surveys, preserving features and reducing bias.
Contribution
The paper presents a novel full-sky lensing map making algorithm that combines deblurring and convergence inversion with edge-preserving penalties, improving reconstruction quality in masked surveys.
Findings
Effective deblurring in crowded fields for ground-based surveys.
Accurate reconstruction of convergence maps with minimal bias near clusters.
Preservation of map topology and statistics despite Galactic cuts.
Abstract
Within the context of upcoming full-sky lensing surveys, the edge-preserving non- linear algorithm Aski is presented. Using the framework of Maximum A Posteriori inversion, it aims at recovering the full-sky convergence map from surveys with masks. It proceeds in two steps: CCD images of crowded galactic fields are deblurred using automated edge-preserving deconvolution; once the reduced shear is estimated, the convergence map is also inverted via an edge- preserving method. For the deblurring, it is found that when the observed field is crowded, this gain can be quite significant for realistic ground-based surveys when both positivity and edge-preserving penalties are imposed during the iterative deconvolution. For the convergence inversion, the quality of the reconstruction is investigated on noisy maps derived from the horizon N-body simulation, with and without Galactic cuts, and…
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