Towards solving model bias in cosmic shear forward modeling. (arXiv:2210.16243v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Remy_B/0/1/0/all/0/1">Benjamin Remy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lanusse_F/0/1/0/all/0/1">Francois Lanusse</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Starck_J/0/1/0/all/0/1">Jean-Luc Starck</a>
As the volume and quality of modern galaxy surveys increase, so does the
difficulty of measuring the cosmological signal imprinted in galaxy shapes.
Weak gravitational lensing sourced by the most massive structures in the
Universe generates a slight shearing of galaxy morphologies called cosmic
shear, key probe for cosmological models. Modern techniques of shear estimation
based on statistics of ellipticity measurements suffer from the fact that the
ellipticity is not a well-defined quantity for arbitrary galaxy light profiles,
biasing the shear estimation. We show that a hybrid physical and deep learning
Hierarchical Bayesian Model, where a generative model captures the galaxy
morphology, enables us to recover an unbiased estimate of the shear on
realistic galaxies, thus solving the model bias.
As the volume and quality of modern galaxy surveys increase, so does the
difficulty of measuring the cosmological signal imprinted in galaxy shapes.
Weak gravitational lensing sourced by the most massive structures in the
Universe generates a slight shearing of galaxy morphologies called cosmic
shear, key probe for cosmological models. Modern techniques of shear estimation
based on statistics of ellipticity measurements suffer from the fact that the
ellipticity is not a well-defined quantity for arbitrary galaxy light profiles,
biasing the shear estimation. We show that a hybrid physical and deep learning
Hierarchical Bayesian Model, where a generative model captures the galaxy
morphology, enables us to recover an unbiased estimate of the shear on
realistic galaxies, thus solving the model bias.
http://arxiv.org/icons/sfx.gif
