Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userValidating a minimal galaxy bias method for cosmological parameter inference using HSC-SDSS mock catalogs
149
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We assess the performance of a perturbation theory inspired method for inferring cosmological parameters from the joint measurements of galaxy-galaxy weak lensing ($DeltaSigma$) and the projected galaxy clustering ($w_{rm p}$). To do this, we use a wide variety of mock galaxy catalogs constructed based on a large set of $N$-body simulations that mimic the Subaru HSC-Y1 and SDSS galaxies, and apply the method to the mock signals to address whether to recover the underlying true cosmological parameters in the mocks. We find that, as long as the appropriate scale cuts, $12$ and $8~h^{-1}{rm Mpc}$ for $DeltaSigma$ and $w_{rm p}$ respectively, are adopted, a minimal-bias model using the linear bias parameter $b_1$ alone and the nonlinear matter power spectrum can recover the true cosmological parameters (here focused on $Omega_{rm m}$ and $sigma_8$) to within the 68% credible interval, for all the mocks we study including one in which an assembly bias effect is implemented. This is as expected if physical processes inherent in galaxy formation/evolution are confined to local, small scales below the scale cut, and thus implies that real-space observables have an advantage in filtering out the impact of small-scale nonlinear effects in parameter estimation, compared to their Fourier-space counterparts. In addition, we find that a theoretical template including the higher-order bias contributions such as nonlinear bias parameter $(b_2)$ does not improve the cosmological constraints, but rather leads to a larger parameter bias compared to the baseline $b_1$-method.
rate research
Read More
We present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, $w_{rm p}(R)$, and the galaxy-galaxy weak lensing, $Delta!Sigma(R)$, from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of $N$-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of $Delta!Sigma$ and $w_{rm p}$ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {it if} the analysis is restricted to scales $Rgtrsim 10~h^{-1}{rm Mpc}$. We also show that, by using a single population of source galaxies to infer the relative strengths of $Delta!Sigma$ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of $sigma(S_8)simeq 0.04$.
We study the topology of the matter density field in two dimensional slices, and consider how we can use the amplitude $A$ of the genus for cosmological parameter estimation. Using the latest Horizon Run 4 simulation data, we calculate the genus of the smoothed density field constructed from lightcone mock galaxy catalogs. Information can be extracted from the amplitude of the genus by considering both its redshift evolution and magnitude. The constancy of the genus amplitude with redshift can be used as a standard population, from which we derive constraints on the equation of state of dark energy $w_{rm de}$ - by measuring $A$ at $z sim 0.1$ and $z sim 1$, we can place an order $Delta w_{rm de} sim {cal O}(15%)$ constraint on $w_{rm de}$. By comparing $A$ to its Gaussian expectation value we can potentially derive an additional stringent constraint on the matter density $Delta Omega_{rm mat} sim 0.01$. We discuss the primary sources of contamination associated with the two measurements - redshift space distortion and shot noise. With accurate knowledge of galaxy bias, we can successfully remove the effect of redshift space distortion, and the combined effect of shot noise and non-linear gravitational evolution is suppressed by smoothing over suitably large scales $R_{rm G} ge 15 {rm Mpc}/h$. Without knowledge of the bias, we discuss how joint measurements of the two and three dimensional genus can be used to constrain the growth factor $beta = f/b$. The method can be applied optimally to redshift slices of a galaxy distribution generated using the drop-off technique.
We use mock galaxy survey simulations designed to resemble the Dark Energy Survey Year 1 (DES Y1) data to validate and inform cosmological parameter estimation. When similar analysis tools are applied to both simulations and real survey data, they provide powerful validation tests of the DES Y1 cosmological analyses presented in companion papers. We use two suites of galaxy simulations produced using different methods, which therefore provide independent tests of our cosmological parameter inference. The cosmological analysis we aim to validate is presented in DES Collaboration et al. (2017) and uses angular two-point correlation functions of galaxy number counts and weak lensing shear, as well as their cross-correlation, in multiple redshift bins. While our constraints depend on the specific set of simulated realisations available, for both suites of simulations we find that the input cosmology is consistent with the combined constraints from multiple simulated DES Y1 realizations in the $Omega_m-sigma_8$ plane. For one of the suites, we are able to show with high confidence that any biases in the inferred $S_8=sigma_8(Omega_m/0.3)^{0.5}$ and $Omega_m$ are smaller than the DES Y1 $1-sigma$ uncertainties. For the other suite, for which we have fewer realizations, we are unable to be this conclusive; we infer a roughly 70% probability that systematic biases in the recovered $Omega_m$ and $S_8$ are sub-dominant to the DES Y1 uncertainty. As cosmological analyses of this kind become increasingly more precise, validation of parameter inference using survey simulations will be essential to demonstrate robustness.
The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data. These summary statistics are learned automatically using an information maximising loss. Given an observation, we further show how one can use the constructed estimators to obtain approximate Bayes computation (ABC) posterior estimates and their corresponding uncertainties that can be used for parameter inference using Gaussian process regression even if the likelihood is not tractable. We validate our method with an application to the problem of cosmological parameter inference of weak lensing mass maps. We show in that case that the constructed estimators are unbiased and have an almost optimal variance, while the posterior distribution obtained with the Gaussian process regression is close to the true posterior and performs better or equally well than comparable methods.
We present 2000 mock galaxy catalogs for the analysis of baryon acoustic oscillations in the Emission Line Galaxy (ELG) sample of the Extended Baryon Oscillation Spectroscopic Survey Data Release 16 (eBOSS DR16). Each mock catalog has a number density of $6.7 times 10^{-4} h^3 rm Mpc^{-3}$, covering a redshift range from 0.6 to 1.1. The mocks are calibrated to small-scale eBOSS ELG clustering measurements at scales of around 10 $h^{-1}$Mpc. The mock catalogs are generated using a combination of GaLAxy Mocks (GLAM) simulations and the Quick Particle-Mesh (QPM) method. GLAM simulations are used to generate the density field, which is then assigned dark matter halos using the QPM method. Halos are populated with galaxies using a halo occupation distribution (HOD). The resulting mocks match the survey geometry and selection function of the data, and have slightly higher number density which allows room for systematic analysis. The large-scale clustering of mocks at the baryon acoustic oscillation (BAO) scale is consistent with data and we present the correlation matrix of the mocks.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
