- Raytrace through overdensity Healpix maps to return a convergence map
- Include shear-kappa transformation on the full sphere
- Include intrinsic alignments (NLA model)
Requirements (python3):
numpy
scipy
astropy
healpy
If you find this code useful, please cite: "Likelihood-free inference with neural compression of DES SV weak lensing map statistics", [Jeffrey, Alsing, Lanusse 2020]
article{2020,
title={Likelihood-free inference with neural compression of DES SV weak lensing map statistics},
volume={501},
ISSN={1365-2966},
url={http://dx.doi.org/10.1093/mnras/staa3594},
DOI={10.1093/mnras/staa3594},
number={1},
journal={Monthly Notices of the Royal Astronomical Society},
publisher={Oxford University Press (OUP)},
author={Jeffrey, Niall and Alsing, Justin and Lanusse, François},
year={2020},
month={Nov},
pages={954–969}
}
The weak lensing convergence κ is given by a weighted projection of the density along the line of sight from the observer to a point with radial comoving distance χ and angular position φ on the sky
where H_0 is the present value of the Hubble parameter, a is the cosmological scale factor, Ω_m is the matter density parameter, δ is the overdensity, and the speed of light c=1. We have assumed flatness, such that the cosmological global curvature is zero, K=0.
In the demo, at a given sky position this is currently implemented as
For a radial (redshift) distribution n(χ ) of lensed source galaxies, the convergence is given by
where
Included in the repository are example Healpix simulated density maps described by (https://arxiv.org/abs/1706.01472) available: http://cosmo.phys.hirosaki-u.ac.jp/takahasi/allsky_raytracing/
The BornRaytrace code is validated against the results from GRayTrace (http://th.nao.ac.jp/MEMBER/hamanatk/GRayTrix/index.html) using these simulations.