%0 Journal Article
%T Towards noiseless gravitational lensing simulations
%+ Institut d'Astrophysique de Paris (IAP)
%A Angulo, Raul E.
%A Chen, Ruizhu
%A Hilbert, Stefan
%A Abel, Tom
%< avec comitÃ© de lecture
%@ 0035-8711
%J Monthly Notices of the Royal Astronomical Society
%I Oxford University Press (OUP): Policy P - Oxford Open Option A
%V 444
%P 2925-2937
%8 2014
%D 2014
%Z 1309.1161
%Z 2014MNRAS.444.2925A
%R 10.1093/mnras/stu1608
%K gravitational lensing: strong
%K gravitational lensing: weak
%K methods: numerical
%K cosmology: theory
%K dark matter
%K large-scale structure of the Universe
%K Astrophysics - Cosmology and Nongalactic Astrophysics
%Z Sciences of the Universe [physics]
%Z Sciences of the Universe [physics]/Astrophysics [astro-ph]Journal articles
%X The microphysical properties of the dark matter (DM) particle can, in principle, be constrained by the properties and abundance of substructures in galaxy clusters, as measured through strong gravitational lensing. Unfortunately, there is a lack of accurate theoretical predictions for the lensing signal of these substructures, mainly because of the discreteness noise inherent to N-body simulations. Here, we present a method, dubbed as Recursive-TCM, that is able to provide lensing predictions with an arbitrarily low discreteness noise. This solution is based on a novel way of interpreting the results of N-body simulations, where particles simply trace the evolution and distortion of Lagrangian phase-space volume elements. We discuss the advantages and limitations of this method compared to the widely used density estimators based on cloud-in-cells and adaptive-kernel smoothing. Applying the new method to a cluster-sized DM halo simulated in warm and cold DM scenarios, we show how the expected differences in their substructure population translate into differences in convergence and magnification maps. We anticipate that our method will provide the high-precision theoretical predictions required to interpret and fully exploit strong gravitational lensing observations.
%G English
%2 https://insu.hal.science/insu-03645251/document
%2 https://insu.hal.science/insu-03645251/file/stu1608.pdf
%L insu-03645251
%U https://insu.hal.science/insu-03645251
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