Cosmological inference from the emulator based halo model II: Joint analysis of galaxy-galaxy weak lensing and galaxy clustering from HSC-Y1 and SDSS

Abstract

We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing (∆Σ) and projected galaxy clustering (wp) measured from the Hyper Suprime-Cam Year-1 (HSC-Y1) data and spectroscopic Sloan Digital Sky Survey (SDSS) galaxy catalogs in the redshift range 0.15 < z < 0.7. We define luminosity-limited samples of SDSS galaxies to serve as the tracers of wp in three spectroscopic redshift bins, and as the lens samples for ∆Σ. For the ∆Σ measurements, we select a single sample of 4 million source galaxies over 140 deg 2 from HSC-Y1 with photometric redshifts (photo-z) greater than 0.75, enabling a better handle of photo-z errors by comparing the ∆Σ amplitudes for the three lens redshift bins. The deep, high-quality HSC-Y1 data enable significant detections of the ∆Σ signals, with integrated signal-to-noise ratio S/N ∼ 15 in the range 3 ≤ R/[h −1 Mpc] ≤ 30 for the three lens samples, despite the small area coverage. For cosmological parameter inference, we use an input galaxy-halo connection model built on the Dark

Cluster cosmology with anisotropic boosts: Validation of a novel forward modeling analysis and application on SDSS redMaPPer clusters

“…where 𝐶 cm (𝐾) and 𝑃 cm (𝑘) are the 2D and 3D cluster-matter power spectra with the 2D wavenumber 𝐾 and the 3D wavenumber 𝑘, and 𝐽 2 is the second-order Bessel function. The last line uses the the Limber approximation (Limber 1954;Kaiser 1992), which becomes exact under our assumption of a single redshift value (also see Hikage et al 2013;Miyatake et al 2021). Lastly, we model the cluster clustering signal.…”

The halo model with beyond-linear halo bias: unbiasing cosmological constraints from galaxy-galaxy lensing and clustering

“…In a recent joint HSC-BOSS galaxy-galaxy lensing and clustering analysis, Miyatake et al (2021) employ the N-body simulations from the D E to model their observables (Nishimichi et al 2019;Miyatake et al 2020). This approach naturally incorporates non-linear halo bias, as the halos are extracted directly from the simulations.…”

“…This approach therefore automatically accounts for the non-linear halo bias and halo exclusion, that we have encapsulated with the D E estimates of 𝛽 NL . As Dark Quest is a dark-matter only simulation, Miyatake et al (2021) then use a halo occupation distribution (HOD) to map the galaxy-halo connection and predict 𝑤 𝑝 (𝑟) and ΔΣ(𝑟) observables to compare with HSC observations and set tight constraints on 𝑆 8 and Ω m .…”

“…Miyatake et al (2021) present cosmological parameter constraints from a joint lensing-clustering analysis of the Hyper Suprime-Cam Survey(HSC, Aihara et al 2017) and the Baryon Oscillation Spectroscopic Survey (BOSS,…”

Differentiable Predictions for Large Scale Structure with SHAMNet

“…An alternative approach is to model the connection between dark matter halos and the galaxies residing within them. The conventional "halo occupation model" approach proceeds with an initial step in which a fitting function and/or a machine learning algorithm is calibrated to capture the cosmology-dependence of various summary statistics of dark matter halos, such as the halo mass function (e.g., Jenkins et al 2001;McClintock et al 2019a;Bocquet et al 2020) and halo bias (e.g, Tinker et al 2010;McClintock et al 2019b); once the approximations for these quantities are specified, models for the galaxy-halo connection determine the prediction for the clustering and lensing of galaxies, enabling the derivation of constraints on cosmological parameters (Cacciato et al 2013;Reddick et al 2014;Miyatake et al 2021).…”