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We simulate the scientific performance of the Nancy Grace Roman Space Telescope High Latitude Survey (HLS) on dark energy and modified gravity. The 1.6-yr HLS Reference survey is currently envisioned to image 2000 deg2 in multiple bands to a depth of ∼26.5 in Y ,  J ,  H and to cover the same area with slit-less spectroscopy beyond z  = 3. The combination of deep, multiband photometry and deep spectroscopy will allow scientists to measure the growth and geometry of the Universe through a variety of cosmological probes (e.g. weak lensing, galaxy clusters, galaxy clustering, BAO, Type Ia supernova) and, equally, it will allow an exquisite control of observational and astrophysical systematic effects. In this paper, we explore multiprobe strategies that can be implemented, given the telescope's instrument capabilities. We model cosmological probes individually and jointly and account for correlated systematics and statistical uncertainties due to the higher order moments of the density field. We explore different levels of observational systematics for the HLS survey (photo -z and shear calibration) and ultimately run a joint likelihood analysis in N- dim parameter space. We find that the HLS reference survey alone can achieve a standard dark energy FoM of >300 when including all probes. This assumes no information from external data sets, we assume a flat universe however, and includes realistic assumptions for systematics. Our study of the HLS reference survey should be seen as part of a future community-driven effort to simulate and optimize the science return of the Roman Space Telescope. [ABSTRACT FROM AUTHOR]

We explore synergies between the Nancy Grace Roman Space Telescope and the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). Specifically, we consider scenarios where the currently envisioned survey strategy for the Roman Space Telescope 's High Latitude Survey (HLS reference), i.e. 2000 deg2 in four narrow photometric bands is altered in favour of a strategy of rapid coverage of the LSST area (to full LSST depth) in one band. We find that in only five months, a survey in the W -band can cover the full LSST survey area providing high-resolution imaging for >95 per cent of the LSST Year 10 gold galaxy sample. We explore a second, more ambitious scenario where the Roman Space Telescope spends 1.5 yr covering the LSST area. For this second scenario, we quantify the constraining power on dark energy equation-of-state parameters from a joint weak lensing and galaxy clustering analysis. Our survey simulations are based on the Roman Space Telescope exposure-time calculator and redshift distributions from the CANDELS catalogue. Our statistical uncertainties account for higher order correlations of the density field, and we include a wide range of systematic effects, such as uncertainties in shape and redshift measurements, and modelling uncertainties of astrophysical systematics, such as galaxy bias, intrinsic galaxy alignment, and baryonic physics. We find a significant increase in constraining power for the joint LSST + HLS wide survey compared to LSST Y10 (FoMHLSwide = 2.4 FoMLSST) and compared to LSST + HLS (FoMHLSwide = 5.5 FoMHLSref). [ABSTRACT FROM AUTHOR]

With current and upcoming experiments such as the Wide Field Infrared Survey Telescope, Euclid, and Large Synoptic Survey Telescope, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data set behaves like a high-dimensional nonlinear surface, an excellent target for machine learning. In this work, we use a lightcone of semianalytic galaxies tuned to match Cosmic Assembly Near-infrared Deep Legacy Survey (CANDELS) observations from Lu et al. to train a set of neural networks on a set of galaxy physical properties. We add realistic photometric noise and use trained neural networks to predict stellar masses and average star formation rates (SFRs) on real CANDELS galaxies, comparing our predictions to SED-fitting results. On semianalytic galaxies, we are nearly competitive with template-fitting methods, with biases of 0.01 dex for stellar mass, 0.09 dex for SFR, and 0.04 dex for metallicity. For the observed CANDELS data, our results are consistent with template fits on the same data at 0.15 dex bias in and 0.61 dex bias in the SFR. Some of the bias is driven by SED-fitting limitations, rather than limitations on the training set, and some is intrinsic to the neural network method. Further errors are likely caused by differences in noise properties between the semianalytic catalogs and data. Our results show that galaxy physical properties can in principle be measured with neural networks at a competitive degree of accuracy and precision to template-fitting methods. [ABSTRACT FROM AUTHOR]

Although photometric redshifts (photo- z 's) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and colour. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo- z predictions as a function of magnitude and colour, and illustrate that the underlying redshift distribution at fixed colour can evolve strongly as a function of magnitude. We then test the robustness of the galaxy–galaxy lensing signal in 120 deg2 of HSC–SSP DR1 data to spectroscopic completeness and photo- z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo- z -based weak lensing predictions in future surveys such as LSST and WFIRST. [ABSTRACT FROM AUTHOR]

In the past few years, several independent collaborations have presented cosmological constraints from tomographic cosmic shear analyses. These analyses differ in many aspects: the data sets, the shear and photometric redshift estimation algorithms, the theory model assumptions, and the inference pipelines. To assess the robustness of the existing cosmic shear results, we present in this paper a unified analysis of four of the recent cosmic shear surveys: the Deep Lens Survey (DLS), the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS), the Science Verification data from the Dark Energy Survey (DES-SV), and the 450 deg2 release of the Kilo-Degree Survey (KiDS-450). By using a unified pipeline, we show how the cosmological constraints are sensitive to the various details of the pipeline. We identify several analysis choices that can shift the cosmological constraints by a significant fraction of the uncertainties. For our fiducial analysis choice, considering a Gaussian covariance, conservative scale cuts, assuming no baryonic feedback contamination, identical cosmological parameter priors and intrinsic alignment treatments, we find the constraints (mean, 16 per cent and 84 per cent confidence intervals) on the parameter S 8 ≡ σ8(Ωm/0.3)0.5 to be |$S_{8}=0.942_{-0.045}^{+0.046}$| (DLS), |$0.657_{-0.070}^{+0.071}$| (CFHTLenS), |$0.844_{-0.061}^{+0.062}$| (DES-SV), and |$0.755_{-0.049}^{+0.048}$| (KiDS-450). From the goodness-of-fit and the Bayesian evidence ratio, we determine that amongst the four surveys, the two more recent surveys, DES-SV and KiDS-450, have acceptable goodness of fit and are consistent with each other. The combined constraints are |$S_{8}=0.790^{+0.042}_{-0.041}$|⁠, which is in good agreement with the first year of DES cosmic shear results and recent CMB constraints from the Planck satellite. [ABSTRACT FROM AUTHOR]

We present results from a set of simulations designed to constrain the weak lensing shear calibration for the Hyper Suprime-Cam (HSC) survey. These simulations include HSC observing conditions and galaxy images from the Hubble Space Telescope (HST), with fully realistic galaxy morphologies and the impact of nearby galaxies included. We find that the inclusion of nearby galaxies in the images is critical to reproducing the observed distributions of galaxy sizes and magnitudes, due to the non-negligible fraction of unrecognized blends in ground-based data, even with the excellent typical seeing of the HSC survey (0.58 arcsec in the i band). Using these simulations, we detect and remove the impact of selection biases due to the correlation of weights and the quantities used to define the sample (S/N and apparent size) with the lensing shear. We quantify and remove galaxy property-dependent multiplicative and additive shear biases that are intrinsic to our shear estimation method, including an ∼10 per cent-level multiplicative bias due to the impact of nearby galaxies and unrecognized blends. Finally, we check the sensitivity of our shear calibration estimates to other cuts made on the simulated samples, and find that the changes in shear calibration are well within the requirements for HSC weak lensing analysis. Overall, the simulations suggest that the weak lensing multiplicative biases in the first-year HSC shear catalogue are controlled at the 1 per cent level. [ABSTRACT FROM AUTHOR]

The use of large, X-ray-selected Galaxy cluster catalogues for cosmological analyses requires a thorough understanding of the X-ray mass estimates. Weak gravitational lensing is an ideal method to shed light on such issues, due to its insensitivity to the cluster dynamical state. We perform a weak lensing calibration of 166 galaxy clusters from the REFLEX and BCS cluster catalogue and compare our results to the X-ray masses based on scaled luminosities from that catalogue. To interpret the weak lensing signal in terms of cluster masses, we compare the lensing signal to simple theoretical Navarro-Frenk-White models and to simulated cluster lensing profiles, including complications such as cluster substructure, projected large-scale structure and Eddington bias. We find evidence of underestimation in the X-ray masses, as expected, with ‹MX/MWL› = 0.75 ± 0.07 stat. ±0.05 sys. for our best-fitting model. The biases in cosmological parameters in a typical cluster abundance measurement that ignores this mass bias will typically exceed the statistical errors. [ABSTRACT FROM AUTHOR]

We perform a measurement of the mass-richness relation of the redMaPPer galaxy cluster catalogue using weak lensing data from the Sloan Digital Sky Survey (SDSS). We have carefully characterized a broad range of systematic uncertainties, including shear calibration errors, photo-z biases, dilution by member galaxies, source obscuration, magnification bias, incorrect assumptions about cluster mass profiles, cluster centring, halo triaxiality and projection effects. We also compare measurements of the lensing signal from two independently produced shear and photometric redshift catalogues to characterize systematic errors in the lensing signal itself. Using a sample of 5570 clusters from 0.1 ≤ z ≤ 0.33, the normalization of our power-law mass versus λ relation is log10[M200m/h-1☉] = 14.344 ± 0.021 (statistical) ±0.023 (systematic) at a richness λ=40, a 7 per cent calibration uncertainty, with a power-law index of 1.33+0.09 -0.10 (1σ). The detailed systematics characterization in this work renders it the definitive weak lensing mass calibration for SDSS redMaPPer clusters at this time. [ABSTRACT FROM AUTHOR]

The shapes of distant galaxies are sheared by intervening galaxy clusters. We examine this effect in Stripe 82, a 275 deg² region observed multiple times in the Sloan Digital Sky Survey (SDSS) and co-added to achieve greater depth. We obtain a mass-richness calibration that is similar to other SDSS analyses, demonstrating that the co-addition process did not adversely affect the lensing signal. We also propose a new parameterization of the effect of tomography on the cluster lensing signal which does not require binning in redshift, and we show that using this parameterization we can detect tomography for stacked clusters at varying redshifts. Finally, due to the sensitivity of the tomographic detection to accurately marginalize over the effect of the cluster mass, we show that tomography at low redshift (where dependence on exact cosmological models is weak) can be used to constrain mass profiles in clusters. [ABSTRACT FROM AUTHOR]

We present and describe a catalog of galaxy photometric redshifts (photo-z) for the Sloan Digital Sky Survey (SDSS) Co-add Data. We use the artificial neural network (ANN) technique to calculate the photo-z and the nearest neighbor error method to estimate photo-z errors for ~13 million objects classified as galaxies in the co-add with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of ~83,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey, the Deep Extragalactic Evolutionary Probe Data Release 3, the VIsible imaging Multi-Object Spectrograph--Very Large Telescope Deep Survey, and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than σ68 = 0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data. [ABSTRACT FROM AUTHOR]

We present measurements of cosmic shear two-point correlation functions (TPCFs) from Hyper Suprime-Cam Subaru Strategic Program (HSC) first-year data, and derive cosmological constraints based on a blind analysis. The HSC first-year shape catalog is divided into four tomographic redshift bins ranging from |$z=0.3$| to 1.5 with equal widths of |$\Delta z =0.3$|⁠. The unweighted galaxy number densities in each tomographic bin are 5.9, 5.9, 4.3, and |$2.4\:$| arcmin |$^{-2}$| from the lowest to highest redshifts, respectively. We adopt the standard TPCF estimators, |$\xi _\pm$|⁠ , for our cosmological analysis, given that we find no evidence of significant B-mode shear. The TPCFs are detected at high significance for all 10 combinations of auto- and cross-tomographic bins over a wide angular range, yielding a total signal-to-noise ratio of 19 in the angular ranges adopted in the cosmological analysis, |$7^{\prime }<\theta <56^{\prime }$| for |$\xi _+$| and |$28^{\prime }<\theta <178^{\prime }$| for |$\xi _-$|⁠. We perform the standard Bayesian likelihood analysis for cosmological inference from the measured cosmic shear TPCFs, including contributions from intrinsic alignment of galaxies as well as systematic effects from PSF model errors, shear calibration uncertainty, and source redshift distribution errors. We adopt a covariance matrix derived from realistic mock catalogs constructed from full-sky gravitational lensing simulations that fully account for survey geometry and measurement noise. For a flat |$\Lambda$| cold dark matter model, we find |$S\,_8 \equiv \sigma _8\sqrt{\Omega _{\rm m}/0.3}=0.804_{-0.029}^{+0.032}$|⁠ , and |$\Omega _{\rm m}=0.346_{-0.100}^{+0.052}$|⁠. We carefully check the robustness of the cosmological results against astrophysical modeling uncertainties and systematic uncertainties in measurements, and find that none of them has a significant impact on the cosmological constraints. [ABSTRACT FROM AUTHOR]

We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey (SDSS) Stripe 82 ugriz imaging data. This survey consists of 275 deg2 of repeated scanning by the SDSS camera over –50° ⩽ α ⩽ 60° and –1.°25 ⩽ δ ⩽ +1.°25 centered on the Celestial Equator. Each piece of sky has ∼20 runs contributing and thus reaches ∼2 mag fainter than the SDSS single pass data, i.e., to r ∼ 23.5 for galaxies. We discuss the image processing of the coaddition, the modeling of the point-spread function (PSF), the calibration, and the production of standard SDSS catalogs. The data have an r-band median seeing of 1.″1 and are calibrated to ⩽1%. Star color-color, number counts, and PSF size versus modeled size plots show that the modeling of the PSF is good enough for precision five-band photometry. Structure in the PSF model versus magnitude plot indicates minor PSF modeling errors, leading to misclassification of stars as galaxies, as verified using VVDS spectroscopy. There are a variety of uses for this wide-angle deep imaging data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements. [ABSTRACT FROM AUTHOR]

Stripe 82 in the Sloan Digital Sky Survey was observed multiple times, allowing deeper images to be constructed by co-adding the data. Here, we analyze the ellipticities of background galaxies in this 275 deg2 region, searching for evidence of distortions due to cosmic shear. We do so using measurements of both the shear-shear correlation function and power spectrum, with the latter determined using both “quadratic” and “pseudo” estimation techniques. We show how we verified these methods using mock catalogs. We also describe our methods for modeling and correcting for the effects of the point-spread function (PSF) in our shape measurements, and we also describe our prescription for estimating photometric redshifts (photo-z's) for our galaxy sample. In particular, we assess the impact of potential systematic effects due to the PSF and to photo-z's, and show that these are under control in our analysis. We find consistent correlation function and power spectrum results, where the E-mode cosmic shear signal is detected in both real and Fourier space with >5σ significance on degree scales, while the B-mode is consistent with zero as expected. The amplitude of the signal constrains the combination of the matter density Ωm and fluctuation amplitude σ8 to be Ω0.7mσ8 = 0.252+0.032– 0.052. [ABSTRACT FROM AUTHOR]