Photo-z Uncertainty Research Articles

Redshift Prediction with Images for Cosmology Using a Bayesian Convolutional Neural Network with Conformal Predictions

In the emerging era of big data astrophysics, large-scale extragalactic surveys will soon provide high-quality imaging for billions of celestial objects to answer major questions in astrophysics such as the nature of dark matter and dark energy. Precision cosmology with surveys requires accurate photometric redshift (photo-z) estimation with well-constrained uncertainties as inputs for weak lensing models to measure cosmological parameters. Machine learning methods have shown promise in optimizing the information gained from galaxy images in photo-z estimation; however, many of these methods are limited in their ability to estimate accurate uncertainties. In this work, we present one of the first applications of Bayesian convolutional neural networks (BCNNs) for photo-z estimation and uncertainties. In addition, we use conformal mapping to calibrate the photo-z uncertainties to achieve good statistical coverage. We use the public GalaxiesML data set of ∼300k galaxies from the Hyper Suprime-Cam survey containing five-band photometric images and known spectroscopic redshifts from 0 < z < 4. We find that the performance is much improved when using images compared to photometry, with the BCNN achieving 0.098 rms error, a standard outlier rate of 3.9%, a 3σ outlier rate of 4.5%, and a bias of 0.0007. The performance drops significantly beyond z > 1.5 due to the relative lack of training data beyond those redshifts. This investigation demonstrates the power of using images directly and we advocate that future photo-z analysis of large-scale surveys include galaxy images.

The CluMPR galaxy cluster-finding algorithm and DESI legacy survey galaxy cluster catalogue

ABSTRACT Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly lensed transients. We here present a new cluster-finding algorithm, CluMPR (Clusters from Masses and Photometric Redshifts), that exploits photometric redshifts (photo-z’s) as well as photometric stellar mass measurements. CluMPR uses a 2D binary search tree to search for overdensities of massive galaxies with similar redshifts on the sky and then probabilistically assigns cluster membership by accounting for photo-z uncertainties. We leverage the deep DESI Legacy Survey grzW1W2 imaging over one-third of the sky to create a catalogue of $\sim 300\, 000$ galaxy cluster candidates out to z = 1, including tabulations of member galaxies and estimates of each cluster’s total stellar mass. Compared to other methods, CluMPR is particularly effective at identifying clusters at the high end of the redshift range considered (z = 0.75–1), with minimal contamination from low-mass groups. These characteristics make it ideal for identifying strongly lensed high-redshift supernovae and quasars that are powerful probes of cosmology, dark matter, and stellar astrophysics. As an example application of this cluster catalogue, we present a catalogue of candidate wide-angle strongly lensed quasars in Appendix C. The nine best candidates identified from this sample include two known lensed quasar systems and a possible changing-look lensed QSO with SDSS spectroscopy. All code and catalogues produced in this work are publicly available (see Data Availability).

Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks

We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as the Legacy Survey of Space and Time (LSST); however, common machine learning methods typically provide only point estimates and lack uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values with uncertainty estimates. We have compiled a galaxy data set from the Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a smaller-scale version of large surveys like LSST. We use this data set to investigate the performance of a neural network and a probabilistic BNN for photo-z estimation and evaluate their performance with respect to LSST photo-z science requirements. We also examine the utility of photo-z uncertainties as a means to reduce catastrophic outlier estimates. The BNN outputs the estimate in the form of a Gaussian probability distribution. We use the mean and standard deviation as the redshift estimate and uncertainty. We find that the BNN can produce accurate uncertainties. Using a coverage test, we find excellent agreement with expectation—67.2% of galaxies between 0 < 2.5 have 1σ uncertainties that cover the spectroscopic value. We also include a comparison to alternative machine learning models using the same data. We find the BNN meets two out of three of the LSST photo-z science requirements in the range 0 < z < 2.5.

Reconstructing the baryonic acoustic oscillations in the presence of photo-z uncertainties

ABSTRACT The reconstruction method has been widely employed to improve the baryon acoustic oscillations (BAO) measurement in spectroscopic survey data analysis. In this study, we explore the reconstruction of the BAO signals in the realm of photometric data. By adapting the Zel’dovich reconstruction technique, we develop a formalism to reconstruct the transverse BAO in the presence of photo-z uncertainties under the plane-parallel approximation. We access the performance of the BAO reconstruction through comoving N-body simulations. The transverse reconstruction potential can be derived by solving a 2D potential equation, with the surface density and the radial potential contribution acting as the source terms. The solution is predominantly determined by the surface density. As is evident in dense samples, such as the matter field, the transverse BAO reconstruction can enhance both the strength of the BAO signals and their cross correlation with the initial conditions. At z = 0, the cross-correlation is increased by a factor of 1.2 at $k_\perp = 0.2 \, \mathrm{Mpc}^{-1}h$ and 1.4 at $k_\perp = 0.3 \, \mathrm{Mpc}^{-1}h$, respectively. We contrast the 2D potential results with the 3D Poisson equation solution, wherein we directly solve the potential equation using the position in photo-z space, and find good agreement. Additionally, we examine the impact of various conditions, such as the smoothing scales and the level of photo-z uncertainties, on the reconstruction results. We envision the straightforward application of this method to survey data.

Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space

We report a ∼5.2σ detection of the kinetic Sunyaev–Zel’dovich (kSZ) effect in Fourier space, by combining the DESI galaxy groups and the Planck data. We use the density-weighted pairwise kSZ power spectrum as the summary statistic, and the detailed procedure of its measurement is presented in this paper. Meanwhile, we analyze the redshift space group density power spectrum to constrain its bias parameters and photo-z uncertainties. These best-fitted parameters are substituted to a nonlinear kSZ model, and we fit the measured kSZ power spectrum with this model to constrain the group optical depth τ¯ . Selected by a varying lower mass threshold M th, the galaxy group catalogs with different median masses ( M˜ ) are constructed from the Data Release 9 data of the DESI Legacy Imaging Surveys. M˜ spans a wide range of ∼1013–1014 M ⊙/h and the heaviest M˜∼1014 M⊙/h is larger than those of most other kSZ detections. When the aperture photometric filter radius θ AP is set to be 4.′2, the M˜=1.75×1013 M⊙/h group sample at the median redshift z˜=0.64 has the highest kSZ detection signal-to-noise ratio = 5.2. By fitting τ¯s from various samples against their M˜ s, we obtain a linear logτ¯−logM˜ relation: logτ¯=γ(logM˜−14)+logβ , in which γ = 0.55 ± 0.1. We also vary the aperture photometric filter radius and measure the τ¯ profiles of group samples, whose constraints on the baryon distribution within and around dark matter halos will be discussed in a companion paper.

Robustness of baryon acoustic oscillations measurements with photometric redshift uncertainties

ABSTRACT We investigate the robustness of baryon acoustic oscillations (BAO) measurements with a photometric galaxy sample using mock galaxy catalogues with various sizes of photometric redshift (photo-z) uncertainties. We first conduct the robustness of BAO measurements, assuming we have a perfect knowledge of photo-z uncertainties. We find that the BAO shift parameter α can be constrained in an unbiased manner even for 3 per cent photometric redshift uncertainties up to z ∼ 1. For instance, α = 1.006 ± 0.078 with 95 per cent confidence level is obtained from 3 per cent photo-z uncertainty data at z = 1.03 using the sample of M* ≥ 1010.25 M⊙ h−2. We also find that a sparse galaxy sample, e.g. &amp;lt;2 × 10−4 [h Mpc−1]3, causes additional noise in the covariance matrix calculation and can bias the constraint on α. Following this, we look into the scenario where incorrect photometric redshift uncertainties are assumed in the fitting model. We find that underestimating the photo-z uncertainty leads to a degradation in the constraining power on α. However, the constrained value of α is not biased. We also quantify the constraining power on Ωm0 assuming the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST)-like covariance and find that the 95 per cent confidence level is σ(Ωm0) ∼ 0.03–0.05 corresponding to the photo-z uncertainties of 1–3 per cent, respectively. Finally, we examine whether the skewness in the photometric redshift can bias the constraint on α and confirm that the constraint on α is unbiased, even assuming a Gaussian photo-z uncertainty in our model.

Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the “z spec” subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (∼50 deg2) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

Combining cosmic shear data with correlated photo-z uncertainties: constraints from DESY1 and HSC-DR1

An accurate calibration of the source redshift distribution p(z) is a key aspect in the analysis of cosmic shear data. This, one way or another, requires the use of spectroscopic or high-quality photometric samples. However, the difficulty to obtain colour-complete spectroscopic samples matching the depth of weak lensing catalogs means that the analyses of different cosmic shear datasets often use the same samples for redshift calibration. This introduces a source of statistical and systematic uncertainty that is highly correlated across different weak lensing datasets, and which must be accurately characterised and propagated in order to obtain robust cosmological constraints from their combination. In this paper we introduce a method to quantify and propagate the uncertainties on the source redshift distribution in two different surveys sharing the same calibrating sample. The method is based on an approximate analytical marginalisation of the p(z) statistical uncertainties and the correlated marginalisation of residual systematics. We apply this method to the combined analysis of cosmic shear data from the DESY1 data release and the HSC-DR1 data, using the COSMOS 30-band catalog as a common redshift calibration sample. We find that, although there is significant correlation in the uncertainties on the redshift distributions of both samples, this does not change the final constraints on cosmological parameters significantly. The same is true also for the impact of residual systematic uncertainties from the errors in the COSMOS 30-band photometric redshifts. Additionally, we show that these effects will still be negligible in Stage-IV datasets. Finally, the combination of DESY1 and HSC-DR1 allows us to constrain the “clumpiness” parameter to S 8 = 0.768+0.021 -0.017. This corresponds to a ∼√(2) improvement in uncertainties with respect to either DES or HSC alone.

Photometric redshift estimation of strongly lensed galaxies

Context.Around 105strongly lensed galaxies are expected to be discovered with upcoming wide-field surveys such asEuclidand the LSST. Utilising these large samples to study the inner structure of lens galaxies requires source redshifts, which are needed to turn lens models into mass measurements. However, obtaining spectroscopic source redshifts for large samples of strong lenses is prohibitive with the current capacity of spectroscopic facilities.Aims.As an alternative to spectroscopy, we study the possibility of obtaining source photometric redshifts (photo-zs) for large samples of strong lenses. We pay particular attention to the problem of blending between the lens and the source light.Methods.Our strategy consists of deblending the source and lens light by simultaneously modelling the lens galaxy and the background source in all available photometric bands, and then feeding the derived source colours to a template-fitting photo-zalgorithm. We describe the lens and the source light with a Sérsic profile, and the lens mass with a singular isothermal ellipsoid. We first test our approach on a simulated sample of lenses. Then, we apply it to 23 real systems with broad-band photometry from the Hyper Suprime-Cam survey.Results.We identify the deviations of the lens light from a Sérsic profile and the contrast between the lens and source image as the main drivers of the source colour measurement error. Although the former is challenging to measure directly for real lenses, we find the latter to be sufficient for evaluating the accuracy of a measured source colour. We split the real sample based on the ratio Λ of the lens to source surface brightness measured at the image locations. In the Λ &lt; 1 regime, the photo-zoutlier fraction is 20%, and the accuracy of photo-zestimation is limited by the performance of the template-fitting process. In the opposite regime, the photo-zoutlier fraction is 75%, and the errors from the source colour measurements dominate the photo-zuncertainty.Conclusions.Measuring source photo-zs for lenses with Λ &lt; 1 poses no particular challenges compared to the isolated galaxy case. For systems with significant lens light contamination, however, improvements in the description of the surface brightness distribution of the lens are required: a single Sérsic model is not sufficiently accurate.

Clustering with general photo-z uncertainties: application to Baryon Acoustic Oscillations

ABSTRACT Photometric data can be analysed using the 3D correlation function ξp to extract cosmological information via e.g. measurement of the Baryon Acoustic Oscillations (BAO). Previous studies modeled ξp assuming a Gaussian photo-z approximation. In this work we improve the modeling by incorporating realistic photo-z distribution. We show that the position of the BAO scale in ξp is determined by the photo-z distribution and the Jacobian of the transformation. The latter diverges at the transverse scale of the separation s⊥, and it explains why ξp traces the underlying correlation function at s⊥, rather than s, when the photo-z uncertainty σz/(1+ z) ≳ 0.02. We also obtain the Gaussian covariance for ξp. Due to photo-z mixing, the covariance of ξp shows strong off-diagonal elements. The high correlation of the data causes some issues to the data fitting. None the less, we find that either it can be solved by suppressing the largest eigenvalues of the covariance or it is not directly related to the BAO. We test our BAO fitting pipeline using a set of mock catalogs. The data set is dedicated for Dark Energy Survey Year 3 (DES Y3) BAO analyses and includes realistic photo-z distributions. The theory template is in good agreement with mock measurement. Based on the DES Y3 mocks, ξp statistic is forecast to constrain the BAO shift parameter α to be 1.001 ± 0.023, which is well consistent with the corresponding constraint derived from the angular correlation function measurements. Thus, ξp offers a competitive alternative for the photometric data analyses.

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts

Abstract The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-z's), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-z. We perform initial redshift fits on Subaru Strategic Program galaxies with deep grizy photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with (z phot − z spec)/(1 + z spec) &lt; 0.10. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-z scatter (σ z ) over those made with typical photo-z uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in σ z compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-z accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing σ z by a factor of 2 compared to the full sample, with one-fifth as many photo-z outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal to noise of galaxy angular power spectra.

Stacked CMB lensing and ISW signals around superstructures in the DESI Legacy Survey

ABSTRACTThe imprints of large-scale structures on the Cosmic Microwave Background (CMB) can be studied via the CMB lensing and Integrated Sachs–Wolfe (ISW) signals. In particular, the stacked ISW signal around supervoids has been claimed in several works to be anomalously high. In this study, we find cluster and void superstructures using four tomographic redshift bins with 0 &amp;lt; z &amp;lt; 0.8 from the DESI Legacy Survey and measure the stacked CMB lensing and ISW signals around them. To compare our measurements with ΛCDM model predictions, we construct a mock catalogue with matched galaxy number density and bias and apply the same photo-z uncertainty as the data. The consistency between the mock and the data is verified via the stacked galaxy density profiles around the superstructures and their quantity. The corresponding lensing convergence and ISW maps are then constructed and compared. The stacked lensing signal agrees with data well except at the highest redshift bin in density peaks, where the mock prediction is significantly higher, by approximately a factor of 1.3. The stacked ISW signal is generally consistent with the mock prediction. We do not obtain a significant signal from voids, AISW = −0.10 ± 0.69, and the signal from clusters, AISW = 1.52 ± 0.72, is at best weakly detected. However, these results are strongly inconsistent with previous claims of ISW signals at many times the level of the ΛCDM prediction. We discuss the comparison of our results with past work in this area and investigate possible explanations for this discrepancy.

Protocluster detection in simulations of HSC–SSP and the 10-yr LSST forecast, using PCcones

ABSTRACT The progenitors of present-day galaxy clusters give important clues about the evolution of the large-scale structure, cosmic mass assembly, and galaxy evolution. Simulations are a major tool for these studies since they are used to interpret observations. In this work, we introduce a set of ‘protocluster-light-cones’, dubbed PCcones. They are mock galaxy catalogues generated from the Millennium Simulation with the L-GALAXIES semi-analytic model. These light-cones were constructed by placing a desired structure at the redshift of interest in the centre of the cone. This approach allows to adopt a set of observational constraints, such as magnitude limits and uncertainties in magnitudes and photometric redshifts (photo-zs) to produce realistic simulations of photometric surveys. We show that photo-zs obtained with PCcones are more accurate than those obtained directly with the Millennium Simulation, mostly due to the difference in how apparent magnitudes are computed. We apply PCcones in the determination of the expected accuracy of protocluster detection using photo-zs in the z = 1−3 range in the wide layer of HSC–SSP and the 10-yr LSST forecast. With our technique, we expect to recover only $\sim 38{{\ \rm per\ cent}}$ and $\sim 43{{\ \rm per\ cent}}$ of all massive galaxy cluster progenitors with more than 70 per cent of purity for HSC–SSP and LSST, respectively. Indeed, the combination of observational constraints and photo-z uncertainties affects the detection of structures critically for both emulations, indicating the need of spectroscopic redshifts to improve detection. We also compare our mocks of the Deep CFHTLS at z &amp;lt; 1.5 with observed cluster catalogues, as an extra validation of the light-cones and methods.

Photo-z outlier self-calibration in weak lensing surveys

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. We show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state w0, but with a 65% degradation when both w0 and wa are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3× 2 analysis, combining cosmic shear, projected galaxy clustering and galaxy—galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed “null correlations”, which are usually ignored in 3× 2 analyses. Despite contributing only ∼ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to LCDM, is publicly available at \\href{https://github.com/EmmanuelSchaan/LaSSI}{https://github.com/EmmanuelSchaan/LaSSI}.

PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1

ABSTRACT The Pan-STARRS1 (PS1) 3π survey is a comprehensive optical imaging survey of three quarters of the sky in the grizy broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 3π Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains 2902 054 648 objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1{{\ \rm per\ cent}}$ for galaxies, $97.8{{\ \rm per\ cent}}$ for stars, and $96.6{{\ \rm per\ cent}}$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δznorm〉 = 0.0005, a standard deviation of σ(Δznorm) = 0.0322, a median absolute deviation of MAD(Δznorm) = 0.0161, and an outlier fraction of $P\left(|\Delta z_{\mathrm{norm}}|\gt 0.15\right)=1.89{{\ \rm per\ cent}}$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

On the relative bias of void tracers in the Dark Energy Survey

Abstract Luminous tracers of large-scale structure are not entirely representative of the distribution of mass in our Universe. As they arise from the highest peaks in the matter density field, the spatial distribution of luminous objects is biased towards those peaks. On large scales, where density fluctuations are mild, this bias simply amounts to a constant offset in the clustering amplitude of the tracer, known as linear bias. In this work we focus on the relative bias between galaxies and galaxy clusters that are located inside and in the vicinity of cosmic voids, extended regions of relatively low density in the large-scale structure of the Universe. With the help of mock data we verify that the relation between galaxy and cluster overdensity around voids remains linear. Hence, the void-centric density profiles of different tracers can be linked by a single multiplicative constant. This amounts to the same value as the relative linear bias between tracers for the largest voids in the sample. For voids of small sizes, which typically arise in higher density regions, this constant has a higher value, possibly showing an environmental dependence similar to that observed for the linear bias itself. We confirm our findings by analysing data obtained during the first year of observations by the Dark Energy Survey. As a side product, we present the first catalogue of three-dimensional voids extracted from a photometric survey with a controlled photo-z uncertainty. Our results will be relevant in forthcoming analyses that attempt to use voids as cosmological probes.

Amico galaxy clusters in KiDS-DR3: sample properties and selection function

We present the first catalogue of galaxy cluster candidates derived from the third data release of the Kilo Degree Survey (KiDS-DR3). The sample of clusters has been produced using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm. In this analysis AMICO takes advantage of the luminosity and spatial distribution of galaxies only, not considering colours. In this way, we prevent any selection effect related to the presence or absence of the red-sequence in the clusters. The catalogue contains 7988 candidate galaxy clusters in the redshift range 0.1<z<0.8 down to S/N>3.5 with a purity approaching 95% over the entire redshift range. In addition to the catalogue of galaxy clusters we also provide a catalogue of galaxies with their probabilistic association to galaxy clusters. We quantify the sample purity, completeness and the uncertainties of the detection properties, such as richness, redshift, and position, by means of mock galaxy catalogues derived directly from the data. This preserves their statistical properties including photo-z uncertainties, unknown absorption across the survey, missing data, spatial correlation of galaxies and galaxy clusters. Being based on the real data, such mock catalogues do not have to rely on the assumptions on which numerical simulations and semi-analytic models are based on. This paper is the first of a series of papers in which we discuss the details and physical properties of the sample presented in this work.

Effects of self-calibration of intrinsic alignment on cosmological parameter constraints from future cosmic shear surveys

Intrinsic alignments (IA) of galaxies have been recognized as one of the most serious contaminants to weak lensing. These systematics need to be isolated and mitigated in order for ongoing and future lensing surveys to reach their full potential. The IA self-calibration (SC) method was shown in previous studies to be able to reduce the GI contamination by up to a factor of 10 for the 2-point and 3-point correlations. The SC method does not require the assumption of an IA model in its working and can extract the GI signal from the same photo-z survey offering the possibility to test and understand structure formation scenarios and their relationship to IA models. In this paper, we study the effects of the IA SC mitigation method on the precision and accuracy of cosmological parameter constraints from future cosmic shear surveys LSST, WFIRST and Euclid. We perform analytical and numerical calculations to estimate the loss of precision and the residual bias in the best fit cosmological parameters after the self-calibration is performed. We take into account uncertainties from photometric redshifts and the galaxy bias. We find that the confidence contours are slightly inflated from applying the SC method itself while a significant increase is due to the inclusion of the photo-z uncertainties. The bias of cosmological parameters is reduced from several-σ, when IA is not corrected for, to below 1-σ after SC is applied. These numbers are comparable to those resulting from applying the method of marginalizing over IA model parameters despite the fact that the two methods operate very differently. We conclude that implementing the SC for these future cosmic-shear surveys will not only allow one to efficiently mitigate the GI contaminant but also help to understand their modeling and link to structure formation.

CAN WE DETECT THE COLOR–DENSITY RELATION WITH PHOTOMETRIC REDSHIFTS?

ABSTRACT A variety of methods have been proposed to define and to quantify galaxy environments. While these techniques work well in general with spectroscopic redshift samples, their application to photometric redshift surveys remains uncertain. To investigate whether galaxy environments can be robustly measured with photo-z samples, we quantify how the density measured with the nearest-neighbor approach is affected by photo-z uncertainties by using the Durham mock galaxy catalogs in which the 3D real-space environments and the properties of galaxies are known exactly. Furthermore, we present an optimization scheme in the choice of parameters used in the 2D projected measurements that yield the tightest correlation with respect to the 3D real-space environments. By adopting the optimized parameters in the density measurements, we show that the correlation between the 2D projected optimized density and the real-space density can still be revealed, and the color–density relation is also visible out to z ∼ 0.8 even for a photo-z uncertainty ( ) up to 0.06. We find that at redshifts 0.3 &lt; z &lt; 0.5 a deep (i ∼ 25) photometric redshift survey with yields a performance in small-scale density measurement that is comparable to a shallower i ∼ 22.5 spectroscopic sample with ∼10% sampling rate. Finally, we discuss the application of the local density measurements to the Pan-STARRS1 Medium Deep Survey (PS-MDS), one of the largest deep optical imaging surveys. Using data from ∼5 square degrees of survey area, our results show that it is possible to measure local density and to probe the color–density relation with 3σ confidence level out to z ∼ 0.8 in the PS-MDS. The color–density relation, however, quickly degrades for data covering smaller areas.

The impact of intrinsic alignment on current and future cosmic shear surveys

Intrinsic alignment (IA) of source galaxies is one of the major astrophysical systematics for ongoing and future weak lensing surveys. This paper presents the first forecasts of the impact of IA on cosmic shear measurements for current and future surveys (DES, Euclid, LSST, WFIRST) using simulated likelihood analyses and realistic covariances that include higher-order moments of the density field in the computation. We consider a range of possible IA scenarios and test mitigation schemes, which parameterize IA by the fraction of red galaxies, normalization, luminosity and redshift dependence of the IA signal (for a subset we consider joint IA and photo-z uncertainties). Compared to previous studies we find smaller biases in time-dependent dark energy models if IA is ignored in the analysis; the amplitude and significance of these biases vary as a function of survey properties (depth, statistical uncertainties), luminosity function, and IA scenario: Due to its small statistical errors and relatively shallow observing strategy Euclid is significantly impacted by IA. LSST and WFIRST benefit from their increased survey depth, while the larger statistical errors for DES decrease IA's relative impact on cosmological parameters. The proposed IA mitigation scheme removes parameter biases due to IA for DES, LSST, and WFIRST even if the shape of the IA power spectrum is only poorly known; successful IA mitigation for Euclid requires more prior information. We explore several alternative IA mitigation strategies for Euclid; in the absence of alignment of blue galaxies we recommend the exclusion of red (IA contaminated) galaxies in cosmic shear analyses. We find that even a reduction of 20% in the number density of galaxies only leads to a 4-10% loss in cosmological constraining power.