scispace - formally typeset
Open AccessJournal ArticleDOI

On the halo-mass and radial scale dependence of the lensing is low effect

Reads0
Chats0
TLDR
In this article, the authors present new measurements and modelling of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) low-lowz sample, focusing on the radial and stellar mass dependence of the lensing amplitude mis-match and find an amplitude mismatch of around $35\%$ when assuming the canonical $Lambda$CDM with Planck Cosmological Microwave Background (CMB) constraints.
Abstract
The canonical $\Lambda$CDM cosmological model makes precise predictions for the clustering and lensing properties of galaxies. It has been shown that the lensing amplitude of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) is lower than expected given their clustering properties. We present new measurements and modelling of galaxies in the BOSS LOWZ sample. We focus on the radial and stellar mass dependence of the lensing amplitude mis-match. We find an amplitude mis-match of around $35\%$ when assuming $\Lambda$CDM with Planck Cosmological Microwave Background (CMB) constraints. This offset is independent of halo mass and radial scale in the range $M_{\rm halo}\sim 10^{13.3} - 10^{13.9} h^{-1} M_\odot$ and $r=0.1 - 60 \, h^{-1} \mathrm{Mpc}$ ($k \approx 0.05 - 20 \, h \, {\rm Mpc}^{-1}$). The observation that the offset is both mass and scale independent places important constraints on the degree to which astrophysical processes (baryonic effects, assembly bias) can fully explain the effect. This scale independence also suggests that the "lensing is low" effect on small and large radial scales probably have the same physical origin. Resolutions based on new physics require a nearly uniform suppression, relative to $\Lambda$CDM predictions, of the amplitude of matter fluctuations on these scales. The possible causes of this are tightly constrained by measurements of the CMB and of the low-redshift expansion history.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing

- 13 Jan 2022 - 
TL;DR: The first cosmology results from large-scale structure in the Dark Energy Survey (DES) spanning 5000 deg$^2 were presented in this paper , where the authors performed an analysis combining three two-point correlation functions (3$\times$2pt): (i) cosmic shear using 100 million source galaxies, (ii) galaxy clustering, and (iii) cross-correlation of source galaxy shear with lens galaxy positions.

Overview of the DESI Legacy Imaging Surveys

TL;DR: The DESI Legacy Imaging Surveys (http://legacysurvey.org/) project is a combination of three public projects (the Dark Energy Camera Legacy Survey, the Beijing-Arizona Sky Survey, and the Mayall z-band Legacy Survey) that will jointly image ≈14,000 deg2 of the extragalactic sky visible from the northern hemisphere in three optical bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and the Cerro Tololo Inter-American Observatory.
Journal ArticleDOI

Arbitrating the S8 discrepancy with growth rate measurements from redshift-space distortions

TL;DR: In this paper, the authors examined the role of measurements of the growth rate in arbitrating the $S_8$ discrepancy, considering measurements of $f\sigma_8(z)$ from Redshift-Space Distortions (RSD) from Baryon Acoustic Oscillations (BAO) and Type Ia Supernovae (SNeIa).
Journal ArticleDOI

Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function

TL;DR: In this article, an extended halo occupation distribution model (HOD) is proposed that includes both a concentration-based assembly bias term and an environment-based bias term, and it achieves a good fit (chi 2/DoF = 1.35) on the 2D redshift-space 2-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample.
Journal ArticleDOI

Consistent lensing and clustering in a low-S8 Universe with BOSS, DES Year 3, HSC Year 1 and KiDS-1000

Alexandra Amon, +107 more
TL;DR: In this article , the authors evaluate the consistency between lensing and clustering based on measurements from BOSS combined with galaxy-galaxy lensing from DES-Y3, HSC-Y1, KiDS-1000.
References
More filters
Journal ArticleDOI

Matplotlib: A 2D Graphics Environment

TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
Journal ArticleDOI

A Universal Density Profile from Hierarchical Clustering

TL;DR: In this article, the authors used high-resolution N-body simulations to study the equilibrium density profiles of dark matter halos in hierarchically clustering universes, and they found that all such profiles have the same shape, independent of the halo mass, the initial density fluctuation spectrum, and the values of the cosmological parameters.
Journal ArticleDOI

Astropy: A community Python package for astronomy

TL;DR: Astropy as discussed by the authors is a Python package for astronomy-related functionality, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions.
Journal ArticleDOI

The NumPy Array: A Structure for Efficient Numerical Computation

TL;DR: In this article, the authors show how to improve the performance of NumPy arrays through vectorizing calculations, avoiding copying data in memory, and minimizing operation counts, which is a technique similar to the one described in this paper.
Journal ArticleDOI

The NumPy array: a structure for efficient numerical computation

TL;DR: This effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.
Related Papers (5)