Towards noiseless gravitational lensing simulations - INSU - Institut national des sciences de l'Univers Accéder directement au contenu
Article Dans Une Revue Monthly Notices of the Royal Astronomical Society Année : 2014

Towards noiseless gravitational lensing simulations

Raul E. Angulo
  • Fonction : Auteur
Ruizhu Chen
  • Fonction : Auteur
Stefan Hilbert
  • Fonction : Auteur

Résumé

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.
Fichier principal
Vignette du fichier
stu1608.pdf (2.76 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

insu-03645251 , version 1 (25-04-2022)

Identifiants

Citer

Raul E. Angulo, Ruizhu Chen, Stefan Hilbert, Tom Abel. Towards noiseless gravitational lensing simulations. Monthly Notices of the Royal Astronomical Society, 2014, 444, pp.2925-2937. ⟨10.1093/mnras/stu1608⟩. ⟨insu-03645251⟩
27 Consultations
14 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More