首页    期刊浏览 2025年04月20日 星期日
登录注册

文章基本信息

  • 标题:Optimizing Simulation Parameters for Weak Lensing Analyses Involving Non-Gaussian Observables
  • 本地全文:下载
  • 作者:José Manuel Zorrilla Matilla ; Stefan Waterval ; Zoltán Haiman
  • 期刊名称:The Astronomical journal
  • 印刷版ISSN:0004-6256
  • 电子版ISSN:1538-3881
  • 出版年度:2020
  • 卷号:159
  • 期号:6
  • 页码:3981-3994
  • DOI:10.3847/1538-3881/ab8f8c
  • 语种:English
  • 出版社:American Institute of Physics
  • 摘要:We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in ray-tracing dark matter (DM)-only simulations, to two hyper-parameters that influence the accuracy as well as the computational cost of the predictions: the thickness of the lens planes used to build past light cones and the mass resolution of the underlying DM simulation.The statistics considered are the power spectrum (PS) and a series of non-Gaussian observables, including the one-point probability density function, lensing peaks, and Minkowski functionals.Counterintuitively, we find that using thin lens planes (< 60 h−1 Mpc on a 240 h−1 Mpc simulation box) suppresses the PS over a broad range of scales beyond what would be acceptable for a survey comparable to the Large Synoptic Survey Telescope (LSST).A mass resolution of 7.2 × 1011 h−1 M⊙ per DM particle (or 2563 particles in a (240 h−1 Mpc)3 box) is sufficient to extract information using the PS and non-Gaussian statistics from weak lensing data at angular scales down to 1' with LSST-like levels of shape noise.
  • 关键词:Weak gravitational lensing;Large-scale structure of the universe;Computational methods
国家哲学社会科学文献中心版权所有