3D cosmic shear: numerical challenges, 3D lensing random fields generation and Minkowski Functionals for cosmological inference
A. Spurio Mancini, P. L. Taylor, R. Reischke, T. Kitching, V., Pettorino, B. M. Sch\"afer, B. Zieser, Ph. M. Merkel

TL;DR
This paper addresses numerical challenges in 3D cosmic shear analysis by comparing integration methods, generating Gaussian random fields, and exploring Minkowski functionals for enhanced cosmological inference beyond traditional statistics.
Contribution
It introduces and compares numerical methods for 3D cosmic shear covariance calculations, and demonstrates the use of Minkowski functionals for cosmological parameter inference.
Findings
Two numerical integration methods are compared for efficiency and accuracy.
A procedure for generating 3D Gaussian random fields from shear covariances is validated.
Minkowski functionals are shown to be effective for parameter inference in cosmic shear surveys.
Abstract
Cosmic shear - the weak gravitational lensing effect generated by fluctuations of the gravitational tidal fields of the large-scale structure - is one of the most promising tools for current and future cosmological analyses. The spherical-Bessel decomposition of the cosmic shear field ("3D cosmic shear") is one way to maximise the amount of redshift information in a lensing analysis and therefore provides a powerful tool to investigate in particular the growth of cosmic structure that is crucial for dark energy studies. However, the computation of simulated 3D cosmic shear covariance matrices presents numerical difficulties, due to the required integrations over highly oscillatory functions. We present and compare two numerical methods and relative implementations to perform these integrations. We then show how to generate 3D Gaussian random fields on the sky in spherical coordinates,…
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