Cosmological Constraints with Void Lensing I: the Simulation-Based Inference Framework

Abstract

We present a Simulation-Based Inference (SBI) framework for cosmological parameter estimation via void lensing analysis. Despite the absence of an analytical model of void lensing, SBI can effectively learn posterior distributions through forward modeling of mock data. We develop a forward modeling pipeline that accounts for both cosmology and the galaxy-halo connection. By training a neural density estimator on simulated data, we infer the posteriors of two cosmological parameters, ${\Omega }_m$ and $S_8$. Validation tests are conducted on posteriors derived from different cosmological parameters and a fiducial sample. The results demonstrate that SBI provides unbiased estimates of mean values and accurate uncertainties. These findings highlight the potential to apply void lensing analysis to observational data even without an analytical void lensing model.

11 pages, 11 figures

References in corpus (35)

• SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems
• The Baryon Oscillation Spectroscopic Survey of SDSS-III
• The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores
• KiDS-1000 Cosmology: Cosmic shear constraints and comparison between two point statistics
• KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints
• First Data Release of the Hyper Suprime-Cam Subaru Strategic Program
• Cosmology from the Chinese Space Station Optical Survey (CSS-OS)
• FastPM: a new scheme for fast simulations of dark matter and halos
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Modeling the clustering and halo occupation distribution of BOSS-CMASS galaxies in the Final Data Release
• Modeling Galaxy-Mass Correlations in Dissipationless Simulations
• Testing Gravity using Cosmic Voids
• Voids in massive neutrino cosmologies
• Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids
• Large-Scale Clustering of Cosmic Voids
• Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data
• Cosmic Shear Cosmology Beyond 2-Point Statistics: A Combined Peak Count and Correlation Function Analysis of DES-Y1
• The power spectrum and bispectrum of SDSS DR11 BOSS galaxies II: cosmological interpretation
• The GIGANTES dataset: precision cosmology from voids in the machine learning era
• Cosmological constraints from galaxy-lensing cross correlations using BOSS galaxies with SDSS and CMB lensing
• Persistent homology in cosmic shear: constraining parameters with topological data analysis
• Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum
• DIVE in the cosmic web: voids with Delaunay Triangulation from discrete matter tracer distributions
• LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
• Euclid: Forecasts from the void-lensing cross-correlation
• Galaxy Clustering Analysis with SimBIG and the Wavelet Scattering Transform
• $\mathrm{S}\mathrm{IM}\mathrm{BIG}$: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering
• KiDS-SBI: Simulation-based inference analysis of KiDS-1000 cosmic shear
• KiDS-1000 Cosmology: Constraints from density split statistics
• Void profile from Planck lensing potential map
• Cosmological parameter constraints from CMB lensing with cosmic voids
• Testing $f(R)$ Gravity With Scale Dependent Cosmic Void Velocity Profiles
• KiDS-1000: cross-correlation with Planck cosmic microwave background lensing and intrinsic alignment removal with self-calibration
• Fast Correlation Function Calculator -- A high-performance pair counting toolkit
• Void Lensing in Cubic Galileon Gravity
• Improving Constraint on ${\Omega }_m$ from SDSS Using Marked Correlation Functions

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