Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy twopoint statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (twopoint function) by joint inference of contaminants in the galaxy overdensity field (onepoint function) using a maximumlikelihood estimator (MLE). We test this methodology with KiloDegree Surveylike mock galaxy catalogues and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the subper cent level and reduces numerous additive and multiplicative systematics from $1.5 \sigma$ to less than $0.1\sigma$ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other twopoint correlation measurements.
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to nonfederal websites. Their policies may differ from this site.

ABSTRACT 
ABSTRACT We present posterior sample redshift distributions for the Hyper SuprimeCam Subaru Strategic Program Weak Lensing threeyear (HSC Y3) analysis. Using the galaxies’ photometry and spatial crosscorrelations, we conduct a combined Bayesian Hierarchical Inference of the sample redshift distributions. The spatial crosscorrelations are derived using a subsample of Luminous Red Galaxies (LRGs) with accurate redshift information available up to a photometric redshift of z < 1.2. We derive the photometrybased constraints using a combination of two empirical techniques calibrated on spectroscopic and multiband photometric data that cover a spatial subset of the shear catalogue. The limited spatial coverage induces a cosmic variance error budget that we include in the inference. Our crosscorrelation analysis models the photometric redshift error of the LRGs to correct for systematic biases and statistical uncertainties. We demonstrate consistency between the sample redshift distributions derived using the spatial crosscorrelations, the photometry, and the posterior of the combined analysis. Based on this assessment, we recommend conservative priors for sample redshift distributions of tomographic bins used in the threeyear cosmological Weak Lensing analyses.

ABSTRACT Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution n(z). Due to the challenge of running Markov chain Monte Carlo (MCMC) in the highdimensional parameter spaces in which the n(z) uncertainties may be parametrized, it is common practice to simplify the n(z) parametrization or combine MCMC chains that each have a fixed n(z) resampled from the n(z) uncertainties. In this work, we propose a statistically principled Bayesian resampling approach for marginalizing over the n(z) uncertainty using multiple MCMC chains. We selfconsistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC threeyear shape catalogue, and find that these methods recover statistically consistent error bars for the cosmological parameter constraints for predicted HSC threeyear analysis, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for data sets with the constraining power of the full HSC survey data set (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over n(z) uncertainty among the several methods from the literature may modify the 1σ uncertainties on Ωm–S8 constraints by ∼4 per cent, and a careful model selection is needed to ensure credible parameter intervals.

ABSTRACT Galaxies exhibit coherent alignments with local structure in the Universe. This effect, called intrinsic alignments (IAs), is an important contributor to the systematic uncertainties for widefield weak lensing surveys. On cosmological distance scales, intrinsic shape alignments have been observed in red galaxies, which are usually bulgedominated; while blue galaxies, which are mostly discdominated, exhibit shape alignments consistent with a null detection. However, discdominated galaxies typically consist of two prominent structures: disc and bulge. Since the bulge component has similar properties as elliptical galaxies and is thought to have formed in a similar fashion, naturally one could ask whether the bulge components exhibit similar alignments as ellipticals? In this paper, we investigate how different components of galaxies exhibit IA in the TNG1001 cosmological hydrodynamical simulation, as well as the dependence of IA on the fraction of stars in rotationdominated structures at $z$ = 0. The measurements were controlled for mass differences between the samples. We find that the bulges exhibit significantly higher IA signals, with a nonlinear alignment model amplitude of $A_I = 2.98^{+0.36}_{0.37}$ compared to the amplitude for the galaxies as a whole (both components), $A_I = 1.13^{+0.37}_{0.35}$. The results for bulges are statistically consistent with those for elliptical galaxies, which have $A_I = 3.47^{+0.57}_{0.57}$. These results highlight the importance of studying galaxy dynamics in order to understand galaxy alignments and their cosmological implications.

ABSTRACT Cosmological weak lensing measurements rely on a precise measurement of the shear twopoint correlation function (2PCF) along with a deep understanding of systematics that affect it. In this work, we demonstrate a general framework for detecting and modelling the impact of PSF systematics on the cosmic shear 2PCF and mitigating its impact on cosmological analysis. Our framework can detect PSF leakage and modelling error from all spin2 quantities contributed by the PSF second and higher moments, rather than just the second moments, using the crosscorrelations between galaxy shapes and PSF moments. We interpret null tests using the HSC Year 3 (Y3) catalogs with this formalism and find that leakage from the spin2 combination of PSF fourth moments is the leading contributor to additive shear systematics, with total contamination that is an orderofmagnitude higher than that contributed by PSF second moments alone. We conducted a mock cosmic shear analysis for HSC Y3 and find that, if uncorrected, PSF systematics can bias the cosmological parameters Ωm and S8 by ∼0.3σ. The traditional second momentbased model can only correct for a 0.1σ bias, leaving the contamination largely uncorrected. We conclude it is necessary to model both PSF second and fourth moment contaminations for HSC Y3 cosmic shear analysis. We also reanalyse the HSC Y1 cosmic shear analysis with our updated systematics model and identify a 0.07σ bias on Ωm when using the more restricted second moment model from the original analysis. We demonstrate how to selfconsistently use the method in both real space and Fourier space, assess shear systematics in tomographic bins, and test for PSF model overfitting.

ABSTRACT In order to prepare for the upcoming widefield cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency of galaxies to naturally align towards overdensities, an effect called intrinsic alignments (IA), can be a major source of systematics in the weak lensing analysis. As the details of galaxy formation and evolution relevant to IA cannot be simulated in practice on such volumes, we propose as an alternative a Deep Generative Model. This model is trained on the IllustrisTNG100 simulation and is capable of sampling the orientations of a population of galaxies so as to recover the correct alignments. In our approach, we model the cosmic web as a set of graphs, where the graphs are constructed for each halo, and galaxy orientations as a signal on those graphs. The generative model is implemented on a Generative Adversarial Network architecture and uses specifically designed GraphConvolutional Networks sensitive to the relative 3D positions of the vertices. Given (sub)halo masses and tidal fields, the model is able to learn and predict scalar features such as galaxy and dark matter subhalo shapes; and more importantly, vector features such as the 3D orientation of the major axis of the ellipsoid and the complex 2D ellipticities. For correlations of 3D orientations the model is in good quantitative agreement with the measured values from the simulation, except for at very small and transition scales. For correlations of 2D ellipticities, the model is in good quantitative agreement with the measured values from the simulation on all scales. Additionally, the model is able to capture the dependence of IA on mass, morphological type, and central/satellite type.

ABSTRACT In the era of precision cosmology and everimproving cosmological simulations, a better understanding of different galaxy components such as bulges and discs will give us new insight into galactic formation and evolution. Based on the fact that the stellar populations of the constituent components of galaxies differ by their dynamical properties, we develop two simple models for galaxy decomposition using the TNG100 cosmological hydrodynamical simulation from the IllustrisTNG project. The first model uses a single dynamical parameter and can distinguish four components: thin disc, thick disc, counterrotating disc, and bulge. The second model uses one more dynamical parameter, was defined in a probabilistic manner, and distinguishes two components: bulge and disc. We demonstrate the improved robustness of these models compared to a widely used method in literature involving cuts on the circularity parameter. The number fraction of discdominated galaxies at a given stellar mass obtained by our models agrees well with observations for masses exceeding log10(M*/M⊙) = 10. The galaxies classified as bulgedominated by the second model are mostly red; however, the population classified as discdominated contains significant number of red galaxies alongside the blue population. The contributions of the different galaxy components to the total stellar mass budget exhibits similar trends with stellar mass compared to the observational data, although there is a quantitative disagreement at high and low masses. The Sérsic indices and halfmass radii for the bulge and disc components agree well with those of real galaxies.more » « less

null (Ed.)ABSTRACT Image simulations are essential tools for preparing and validating the analysis of current and future widefield optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available spacebased data. In this work, we propose a methodology based on deep generative models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and point spread function (PSF)convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the PSF and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second and higher order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce galsimhub, a communitydriven repository of generative models, and a framework for incorporating generative models within the galsim image simulation software.more » « less

ABSTRACT Weak gravitational lensing is one of the most powerful tools for cosmology, while subject to challenges in quantifying subtle systematic biases. The point spread function (PSF) can cause biases in weak lensing shear inference when the PSF model does not match the true PSF that is convolved with the galaxy light profile. Although the effect of PSF size and shape errors – i.e. errors in second moments – is well studied, weak lensing systematics associated with errors in higher moments of the PSF model require further investigation. The goal of our study is to estimate their potential impact for LSST weak lensing analysis. We go beyond second moments of the PSF by using image simulations to relate multiplicative bias in shear to errors in the higher moments of the PSF model. We find that the current level of errors in higher moments of the PSF model in data from the Hyper SuprimeCam survey can induce a ∼0.05 per cent shear bias, making this effect unimportant for ongoing surveys but relevant at the precision of upcoming surveys such as LSST.

null (Ed.)ABSTRACT We investigate the redshift evolution of the intrinsic alignments (IAs) of galaxies in the MassiveBlackII (MBII) simulation. We select galaxy samples above fixed subhalo mass cuts ($M_h\gt 10^{11,12,13}\,\mathrm{M}_{\odot }\, h^{1}$) at z = 0.6 and trace their progenitors to z = 3 along their merger trees. Dark matter components of z = 0.6 galaxies are more spherical than their progenitors while stellar matter components tend to be less spherical than their progenitors. The distribution of the galaxy–subhalo misalignment angle peaks at ∼10 deg with a mild increase with time. The evolution of the ellipticity–direction (ED) correlation amplitude ω(r) of galaxies (which quantifies the tendency of galaxies to preferentially point towards surrounding matter overdensities) is governed by the evolution in the alignment of underlying dark matter (DM) subhaloes to the matter density of field, as well as the alignment between galaxies and their DM subhaloes. At scales $\sim 1~\mathrm{Mpc}\, h^{1}$, the alignment between DM subhaloes and matter overdensity gets suppressed with time, whereas the alignment between galaxies and DM subhaloes is enhanced. These competing tendencies lead to a complex redshift evolution of ω(r) for galaxies at $\sim 1~\mathrm{Mpc}\, h^{1}$. At scales $\gt 1~\mathrm{Mpc}\, h^{1}$, alignment between DM subhaloes and matter overdensity does not evolve significantly; the evolution of the galaxy–subhalo misalignment therefore leads to an increase in ω(r) for galaxies by a factor of ∼4 from z = 3 to 0.6 at scales $\gt 1~\mathrm{Mpc}\, h^{1}$. The balance between competing physical effects is scale dependent, leading to different conclusions at much smaller scales ($\sim 0.1~\mathrm{Mpc}\, h^{1}$).more » « less