Cosmological Parameter Inference Research Articles

Quintessential f(G) gravity with statistically fitting of H(z)

Inspired by high-energy physics and by considering standard gravity theory as the low-energy limit of quantum theory, the modification of general relativity as [Formula: see text] has been proposed. This paper is dedicated to investigating the cosmological model of modified Gauss–Bonnet gravity in four dimensions. General field equations for the four-dimensional Friedmann– Robertson–Walker metric are given in detail. Also, it is well known that Hubble parameter [Formula: see text] measurements are helpful for the inference of cosmological model parameters. We try to explain the MCMC statistical approach used to calculate Hubble’s parameter. The resulting form of [Formula: see text] was limited using Cosmic Chronometric data, Standard Candles SN Ia from Pantheon, and Baryon Acoustic Oscillations to obtain the best match values by using the regression equation. Finally, to examine the late-time dynamics of our Universe, the behavior of physical and kinematic variables like the deceleration parameter [Formula: see text], State-finder parameters [Formula: see text], diagnostic parameter ([Formula: see text]), equation of state parameter as well as the behavior of energy conditions versus redshift with the parameter values are presented.

Lensing bias on cosmological parameters from bright standard sirens

Abstract Next generation gravitational waves (GWs) observatories are expected to measure GW signals with unprecedented sensitivity, opening new, independent avenues to learn about our Universe. The distance-redshift relation is a fulcrum for cosmology and can be tested with GWs emitted by merging binaries of compact objects, called standard sirens, thanks to the fact that they provide the absolute distance from the source. On the other hand, fluctuations of the intervening matter density field induce modifications on the measurement of luminosity distance compared to that of a homogeneous universe. Assuming that the redshift information is obtained through the detection of an electromagnetic counterpart, we investigate the impact that lensing of GWs might have in the inference of cosmological parameters. We treat lensing as a systematic error and check for residual bias on the values of the cosmological parameters. We do so by means of mock catalogues of bright sirens events in different scenarios relevant to Einstein Telescope. For our fiducial scenario, the lensing bias can be comparable to or greater than the expected statistical uncertainty of the cosmological parameters, although non-negligible fluctuations in the bias values are observed for different realisations of the mock catalogue. We also discuss some mitigation strategies that can be adopted in the data analysis. Overall, our work highlights the need to model lensing effects when using standard sirens as probes of the distance-redshift relation.

A Minkowski functional analysis of the Cosmic Microwave Background weak lensing convergence

Minkowski functionals are summary statistics that capture the geometric and morphological properties of fields. They are sensitive to all higher order correlations of the fields and can be used to complement more conventional statistics, such as the power spectrum of the field. We develop a Minkowski functional-based approach for a full likelihood analysis of mildly non-Gaussian sky maps with partial sky coverage. Applying this to the inference of cosmological parameters from the Planck mission's map of the Cosmic Microwave Background's lensing convergence, we find an excellent agreement with results from the power spectrum-based lensing likelihood. While the non-Gaussianity of current CMB lensing maps is dominated by reconstruction noise, a Minkowski functional-based analysis may be able to extract cosmological information from the non-Gaussianity of future lensing maps and thus go beyond what is accessible with a power spectrum-based analysis. We make the numerical code for the calculation of a map's Minkowski functionals, skewness and kurtosis parameters available for download from https://github.com/Kang-Yuqi/MF_lensing.

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

Abstract We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernov aelig; (SNaelig; Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN’s spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalise over 4000 latent variables (for a set of ≈ 100 SNaelig; Ia to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortisation of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNaelig; Ia from the Carnegie Supernova Project,, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model—e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-la contamination—without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples.

Modeling neutrino-induced scale-dependent galaxy clustering for photometric galaxy surveys

The increasing statistical precision of photometric redshift surveys requires improved accuracy of theoretical predictions for large-scale structure observables to obtain unbiased cosmological constraints. In ΛCDM cosmologies, massive neutrinos stream freely at small cosmological scales, suppressing the small-scale power spectrum. In massive neutrino cosmologies, galaxy bias modeling needs to accurately relate the scale-dependent growth of the underlying matter field to observed galaxy clustering statistics. In this work, we implement a computationally efficient approximation of the neutrino-induced scale-dependent bias (NISDB). Through simulated likelihood analyses of Dark Energy Survey Year 3 (DESY3) and Legacy Survey of Space and Time Year 1 (LSSTY1) synthetic data that contain an appreciable NISDB, we examine the impact of linear galaxy bias and neutrino mass modeling choices on cosmological parameter inference. We find model misspecification of the NISDB approximation and neutrino mass models to decrease the constraining power of photometric galaxy surveys and cause parameter biases in the cosmological interpretation of future surveys. We quantify these biases and devise mitigation strategies.

Covariance matrices for the Lyman-α forest using the lognormal approximation

We investigate the nature of correlations in the small-scale flux statistics of the Lyman-α (Lyα) forest across redshift bins. Understanding and characterising these correlations is important for unbiased cosmological and astrophysical parameter inference using the Lyα forest. We focus on the 1-dimensional flux power spectrum (FPS) and mean flux (F̅) simulated using the semi-numerical lognormal model we developed in earlier work. The lognormal model can capture the effects of long wavelength modes with relative ease as compared to full smoothed particle hydrodynamical (SPH) simulations that are limited by box volume. For a single redshift bin of size Δz ≃ 0.1, we show that the lognormal model predicts positive cross-correlations between k-bins in the FPS, and a negative correlation for F̅ × FPS, in qualitative agreement with SPH simulations and theoretical expectations. For measurements across two neighbouring redshift bins of width Δ z each (using long flux skewers of length 2Δ z that are 'split' in half), the lognormal model predicts an anti-correlation for FPS × FPS and a positive correlation for F̅ × FPS, caused by modes with the longest wavelengths. This is in contrast to SPH simulations which predict a negligible magnitude for cross-redshift correlations derived from such `split' skewers, and we discuss possible reasons for this difference. Finally, we perform a preliminary test of the impact of neglecting long wavelength modes on parameter inference, finding that whereas the correlation structure of neighbouring redshift bins has relatively little impact, the absence of long wavelength modes in the model can lead to ≳ 2 - σ biases in the inference of astrophysical parameters. Our results motivate a more careful treatment of long wavelength modes in analyses that rely on the small-scale Lyα forest for parameter inference.

The long-lasting effect of X-ray pre-heating in the post-reionization intergalactic medium

ABSTRACT X-ray photons can penetrate deep into the intergalactic medium (IGM), leading to pre-heating of the IGM prior to cosmic reionization. X-ray pre-heating wipes out some of the small-scale structures that would otherwise be present prior to the passage of an ionization front. Accurate modelling of the small-scale structure is vital to the post-reionization IGM, since the small-scale structure is ultimately the dominant source of long-lasting relics from hydrogen reionization. However, the precise impact of X-ray pre-heating in the fossils from hydrogen reionization is highly uncertain. In this work, we explore and establish for the first time, the long-lasting impact of X-ray pre-heating in the post-reionization IGM via hydrodynamic simulations with high-mass resolution. We find that the addition of X-ray pre-heating astrophysics leads to an overall lesser impact of the effect of inhomogeneous reionization in the Lyα forest – depending on specific X-ray prescription – at low redshifts (z ∼ 2) with respect to a model with no X-ray pre-heating. However, at high redshifts (z ∼ 4), our results indicate a strengthening of the relics of reionization in the Lyα forest because the IGM becomes more transparent compared to the scenario with no pre-heating. Thus, the absence of X-ray pre-heating in Lyα modelling can lead to a biased inference of cosmological parameters. Nevertheless, optimistically, the inclusion of X-ray pre-heating emerges as a promising novel avenue to probe the astrophysics of cosmic dawn.

Machine-learning Cosmology from Void Properties

Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine-learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES data set, where every catalog contains an average of 11,000 voids from a volume of . We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train (1) fully connected neural networks on histograms from individual void properties and (2) deep sets from void catalogs to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of Ω m , σ 8, and n s with mean relative errors of 10%, 4%, and 3%, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.

Towards a full wCDM map-based analysis for weak lensing surveys

ABSTRACT The next generation of weak lensing surveys will measure the matter distribution of the local universe with unprecedented precision, allowing the resolution of non-Gaussian features of the convergence field. This encourages the use of higher-order mass-map statistics for cosmological parameter inference. We extend the forward-modelling based methodology introduced in a previous forecast paper to match these new requirements. We provide multiple forecasts for the $w$CDM parameter constraints that can be expected from stage 3 and 4 weak lensing surveys. We consider different survey setups, summary statistics and mass map filters including wavelets. We take into account the shear bias, photometric redshift uncertainties, and intrinsic alignment. The impact of baryons is investigated and the necessary scale cuts are applied. We compare the angular power spectrum analysis to peak and minima counts as well as Minkowski functionals of the mass maps. We find a preference for Starlet over Gaussian filters. Our results suggest that using a survey setup with 10 instead of 5 tomographic redshift bins is beneficial. Adding cross-tomographic information improves the constraints on cosmology and especially on galaxy intrinsic alignment for all statistics. In terms of constraining power, we find the angular power spectrum and the peak counts to be equally matched for stage 4 surveys, followed by minima counts and the Minkowski functionals. Combining different summary statistics significantly improves the constraints and compensates the stringent scale cuts. We identify the most ‘cost-effective’ combination to be the angular power spectrum, peak counts and Minkowski functionals following Starlet filtering.

CONNECT: a neural network based framework for emulating cosmological observables and cosmological parameter inference

Bayesian parameter inference is an essential tool in modern cosmology, and typically requires the calculation of 105–106 theoretical models for each inference of model parameters for a given dataset combination. Computing these models by solving the linearised Einstein-Boltzmann system usually takes tens of CPU core-seconds per model, making the entire process very computationally expensive.In this paper we present connect, a neural network framework emulating class computations as an easy-to-use plug-in for the popular sampler MontePython. connect uses an iteratively trained neural network which emulates the observables usually computed by class. The training data is generated using class, but using a novel algorithm for generating favourable points in parameter space for training data, the required number of class-evaluations can be reduced by two orders of magnitude compared to a traditional inference run. Once connect has been trained for a given model, no additional training is required for different dataset combinations, making connect many orders of magnitude faster than class (and making the inference process entirely dominated by the speed of the likelihood calculation).For the models investigated in this paper we find that cosmological parameter inference run with connect produces posteriors which differ from the posteriors derived using class by typically less than 0.01–0.1 standard deviations for all parameters. We also stress that the training data can be produced in parallel, making efficient use of all available compute resources. The connect code is publicly available for download on GitHub (https://github.com/AarhusCosmology/connect_public).

Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux-cluster mass relation (YSZ - M), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines YSZ and concentration of ionized gas (cgas): M ∝ Yconc3/5 ≡ YSZ3/5(1 - A cgas). Yconc reduces the scatter in the predicted M by ∼20 - 30% for large clusters (M ≳ 1014 h-1 M⊙), as compared to using just YSZ. We show that the dependence on cgas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Yconc on clusters from CAMELS simulations and show that Yconc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos

We train graph neural networks on halo catalogs from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogs contain ≲5000 halos with masses ≳1010 h −1 M ⊙ in a periodic volume of ; every halo in the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ωm and σ 8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ωm and σ 8 when tested using halo catalogs from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP3M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ωm also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.

Persistent homology in cosmic shear

We demonstrate how to use persistent homology for cosmological parameter inference in a tomographic cosmic shear survey. We obtain the first cosmological parameter constraints from persistent homology by applying our method to the first-year data of the Dark Energy Survey. To obtain these constraints, we analyse the topological structure of the matter distribution by extracting persistence diagrams from signal-to-noise maps of aperture masses. This presents a natural extension to the widely used peak count statistics. Extracting the persistence diagrams from the cosmo-SLICS, a suite of N-body simulations with variable cosmological parameters, we interpolate the signal using Gaussian processes and marginalise over the most relevant systematic effects, including intrinsic alignments and baryonic effects. For the structure growth parameter, we find S8 = 0.747−0.031+0.025, which is in full agreement with other late-time probes. We also constrain the intrinsic alignment parameter to A = 1.54 ± 0.52, which constitutes a detection of the intrinsic alignment effect at almost 3σ.

Cosmological inference from an emulator based halo model. I. Validation tests with HSC and SDSS mock catalogs

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 $R\gtrsim 10~h^{-1}{\rm Mpc}$. 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 $\sigma(S_8)\simeq 0.04$.

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

We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing (ΔΣ) 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 ΔΣ. For the ΔΣ measurements, we select a single sample of 4×106 source galaxies over 140 deg2 from HSC-Y1 with photometric redshifts (photo z) greater than 0.75, enabling a better handle of photo-z errors by comparing the ΔΣ amplitudes for the three lens redshift bins. The deep, high-quality HSC-Y1 data enable significant detections of the ΔΣ 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 emulator package (which uses an ensemble set of high-resolution N-body simulations and enables fast, accurate computation of the clustering observables) with a halo occupation distribution that includes nuisance parameters to marginalize over modeling uncertainties. We model the ΔΣ and wp measurements on scales from R≃3 and 2 h−1 Mpc, respectively, up to 30 h−1 Mpc (therefore excluding the baryon acoustic oscillations information) assuming a flat ΛCDM cosmology, marginalizing over about 20 nuisance parameters and demonstrating the robustness of our results to them. With various tests using mock catalogs described in Miyatake et al. [preceding paper, Phys. Rev. D 106, 083519 (2022)], we show that any bias in the clustering amplitude S8≡σ8(Ωm/0.3)0.5 due to uncertainties in the galaxy-halo connection is less than ∼50% of the statistical uncertainty of S8, unless the assembly biaseffect is unexpectedly large. Our best-fit models have S8=0.795−0.042+0.049 (mode and 68% credible interval) for the flat ΛCDM model; we find tighter constraints on the quantity S8(α=0.17)≡σ8(Ωm/0.3)0.17=0.745−0.031+0.039.23 MoreReceived 29 November 2021Accepted 22 August 2022DOI:https://doi.org/10.1103/PhysRevD.106.083520© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCosmologyDark energyGravitational lensesGravitation, Cosmology & Astrophysics

The Pantheon+ Analysis: The Full Data Set and Light-curve Release

Here we present 1701 light curves of 1550 unique, spectroscopically confirmed Type Ia supernovae (SNe Ia) that will be used to infer cosmological parameters as part of the Pantheon+ SN analysis and the Supernovae and H 0 for the Equation of State of dark energy distance-ladder analysis. This effort is one part of a series of works that perform an extensive review of redshifts, peculiar velocities, photometric calibration, and intrinsic-scatter models of SNe Ia. The total number of light curves, which are compiled across 18 different surveys, is a significant increase from the first Pantheon analysis (1048 SNe), particularly at low redshift (z). Furthermore, unlike in the Pantheon analysis, we include light curves for SNe with z < 0.01 such that SN systematic covariance can be included in a joint measurement of the Hubble constant (H 0) and the dark energy equation-of-state parameter (w). We use the large sample to compare properties of 151 SNe Ia observed by multiple surveys and 12 pairs/triplets of “SN siblings”—SNe found in the same host galaxy. Distance measurements, application of bias corrections, and inference of cosmological parameters are discussed in the companion paper by Brout et al., and the determination of H 0 is discussed by Riess et al. These analyses will measure w with ∼3% precision and H 0 with ∼1 km s−1 Mpc−1 precision.

Covariance of photometric and spectroscopic two-point statistics: Implications for cosmological parameter inference

To combine information from measurements of the redshift-space power spectrum from spectroscopic data with angular weak lensing, galaxy clustering and galaxy-galaxy lensing power spectra from photometric surveys (i.e. the $3 \times 2$ point statistics), we must account for the covariance between the two probes. Currently any covariance between the two types of measurements is neglected as existing photometric and spectroscopic surveys largely probe different cosmological volumes. This will cease to be the case as data arrives from Stage-IV surveys. In this paper we derive an analytic expression for the covariance between photometric 2D angular power spectra and the 3D redshift-space power spectrum for Gaussian fields under the plane-parallel approximation. We find that the two probes are covariant on large radial scales, but because the information content of these modes is extremely low due to sample variance, we forecast that it is safe to neglect this covariance when performing cosmological parameter inference.

Forecasting cosmological parameter constraints using multiple sparsity measurements as tracers of the mass profiles of dark matter haloes

ABSTRACT The dark matter halo sparsity, i.e. the ratio between spherical halo masses enclosing two different overdensities, provides a non-parametric proxy of the halo mass distribution that has been shown to be a sensitive probe of the cosmological imprint encoded in the mass profile of haloes hosting galaxy clusters. Mass estimations at several overdensities would allow for multiple sparsity measurements, which can potentially retrieve the entirety of the cosmological information imprinted on the halo profile. Here, we investigate the impact of multiple sparsity measurements on the cosmological model parameter inference. For this purpose, we analyse N-body halo catalogues from the Raygal and M2Csims simulations and evaluate the correlations among six different sparsities from spherical overdensity halo masses at Δ = 200, 500, 1000, and 2500 (in units of the critical density). Remarkably, sparsities associated to distinct halo mass shells are not highly correlated. This is not the case for sparsities obtained using halo masses estimated from the Navarro-Frenk-White (NFW) best-fitting profile, which artificially correlates different sparsities to order one. This implies that there is additional information in the mass profile beyond the NFW parametrization and that it can be exploited with multiple sparsities. In particular, from a likelihood analysis of synthetic average sparsity data, we show that cosmological parameter constraints significantly improve when increasing the number of sparsity combinations, though the constraints saturate beyond four sparsity estimates. We forecast constraints for the CHEX-MATE cluster sample and find that systematic mass bias errors mildly impact the parameter inference, though more studies are needed in this direction.

Constraining the cosmological parameters using gravitational wave observations of massive black hole binaries and statistical redshift information

Space-borne gravitational wave detectors like TianQin are expected to detect GW signals emitted by the mergers of massive black hole binaries. Luminosity distance information can be obtained from GW observations, and one can perform cosmological inference if redshift information can also be extracted, which would be straightforward if an electromagnetic counterpart exists. In this paper, we concentrate on the conservative scenario where the EM counterparts are not available, and comprehensively study if cosmological parameters can be inferred through a statistical approach, utilizing the non-uniform distribution of galaxies as well as the black hole mass-host galaxy bulge luminosity relationship. By adopting different massive black hole binary merger models, and assuming different detector configurations, we conclude that the statistical inference of cosmological parameters is indeed possible. TianQin is expected to constrain the Hubble constant to a relative error of about 4%-7%, depending on the underlying model. The multidetector network of TianQin and LISA can significantly improve the precision of cosmological parameters. In the most favorable model, it is possible to achieve a level of 1.7% with a network of TianQin and LISA. We find that without EM counterparts, constraints on all other parameters need a larger number of events or more precise sky localization of GW sources, which can be achieved by the multidetector network or under a favorable model for massive black hole mergers. However, in the optimistic case, where EM counterparts are available, one can obtain useful constraints on all cosmological parameters in the Lambda-CDM cosmology, regardless of the population model. Moreover, we can also constrain the equation of state of the dark energy without the EM counterparts, and it is even possible to study the evolution of EoS of the DE when the EM counterparts are observed.

The three-year shear catalog of the Subaru Hyper Suprime-Cam SSP Survey

Abstract We present the galaxy shear catalog that will be used for the three-year cosmological weak gravitational lensing analyses using data from the Wide layer of the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) Survey. The galaxy shapes are measured from the i-band imaging data acquired from 2014 to 2019 and calibrated with image simulations that resemble the observing conditions of the survey based on training galaxy images from the Hubble Space Telescope in the COSMOS region. The catalog covers an area of 433.48 deg2 of the northern sky, split into six fields. The mean i-band seeing is ${0{^{\prime \prime}_{.}}59}$. With conservative galaxy selection criteria (e.g., i-band magnitude brighter than 24.5), the observed raw galaxy number density is 22.9 arcmin−2, and the effective galaxy number density is 19.9 arcmin−2. The calibration removes the galaxy property-dependent shear estimation bias to the level |δm| &amp;lt; 9 × 10−3. The bias residual δm shows no dependence on redshift in the range 0 &amp;lt; z ≤ 3. We define the requirements for cosmological weak-lensing science for this shear catalog, and quantify potential systematics in the catalog using a series of internal null tests for systematics related to point-spread function modelling and shear estimation. A variety of the null tests are statistically consistent with zero or within requirements, but (i) there is evidence for PSF model shape residual correlations; and (ii) star–galaxy shape correlations reveal additive systematics. Both effects become significant on &amp;gt;1° scales and will require mitigation during the inference of cosmological parameters using cosmic shear measurements.