A new model to predict weak-lensing peak counts

2015/06/30 by Chieh-An Lin, Martin Kilbinger, M. Kilbinger · 69 citations
Computer Science · Neuroscience · Physics and Astronomy · #Astrophysics #Blind Source Separation Techniques #Context (archaeology) #Galaxy #Gaussian #Gaussian Processes and Bayesian Inference #Gravitational lensing formalism #Neural dynamics and brain function #Observable #Physics #Quantum mechanics #Redshift #Statistical physics #Strong gravitational lensing #Weak gravitational lensing #astro-ph.CO

paper · pdf · doi:10.1051/0004-6361/201526659

published in Astronomy and Astrophysics 583, A70 (EDP Sciences) · 15 pages, 11 figures. Accepted version

openalex publication_date 2015/09/21 · arxiv created 2015/11/12 · arxiv updated 2015/11/16 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/22

Abstract

Context. Peak counts have been shown to be an excellent tool for extracting the non-Gaussian part of the weak lensing signal. Recently, we developed a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analysis.

Citations

• Optimized detection of shear peaks in weak lensing maps
• The three-point correlation function of cosmic shear
• Estimation of cosmological parameters using adaptive importance sampling
• Shear‐selected Cluster Cosmology: Tomography and Optimal Filtering
• TEASING: a fast and accurate approximation for the low multipole likelihood of the cosmic microwave background temperature
• Probing primordial non-Gaussianity with large-scale structure
• The Three-Point Correlation Function for Spin-2 Fields
• MEASURING PRIMORDIAL NON-GAUSSIANITY THROUGH WEAK-LENSING PEAK COUNTS
• Sequential Monte Carlo Samplers

Cited by

• The effect of intrinsic alignments on weak lensing statistics in hydrodynamical simulations