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Full author list: A Pieres, L Girardi, E Balbinot, B Santiago, L N da Costa, A Carnero Rosell, A B Pace, K Bechtol, M A T Groenewegen, A Drlica-Wagner, T S Li, M A G Maia, R L C Ogando, M dal Ponte, H T Diehl, A Amara, S Avila, E Bertin, D Brooks, D L Burke, M Carrasco Kind, J Carretero, J De Vicente, S Desai, T F Eifler, B Flaugher, P Fosalba, J Frieman, J García-Bellido, E Gaztanaga, D W Gerdes, D Gruen, R A Gruendl, J Gschwend, G Gutierrez, D L Hollowood, K Honscheid, D J James, K Kuehn, N Kuropatkin, J L Marshall, R Miquel, A A Plazas, E Sanchez, S Serrano, I Sevilla-Noarbe, E Sheldon, M Smith, M Soares-Santos, F Sobreira, E Suchyta, M E C Swanson, G Tarle, D Thomas, V Vikram, A R Walker, We present a technique to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) generated from TRILEGAL models to real data. We apply this technique, which we call MWFITTING, to photometric data from the first 3 yr of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby galaxies, the Large Magellanic Cloud, and the Sagittarius Stream, our main sample spans a total area of ~2300 deg2. We further explore a smaller subset (~1300 deg2) that excludes all regions with known stellar streams and stellar overdensities. Validation tests on synthetic data possessing similar properties to the DES data show that the method is able to recover input parameters with a precision better than 3 per cent. We fit the DES data with an exponential thick disc model and an oblate double power-law halo model. We find that the best-fitting thick disc model has radial and vertical scale heights of 2.67 ± 0.09 kpc and 925 ± 40 pc, respectively. The stellar halo is fit with a broken power-law density profile with an oblateness of 0.75 ± 0.01, an inner index of 1.82 ± 0.08, an outer index of 4.14 ± 0.05, and a break at 18.52 ± 0.27 kpc from the Galactic centre. Several previously discovered stellar overdensities are recovered in the residual stellar density map, showing the reliability of MWFITTING in determining the Galactic components. Simulations made with the best-fitting parameters are a promising way to predict Milky Way star counts for surveys such as the LSST and Euclid., The DESDM is supported by the National Science Foundation under grants AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Instituto Nacional de Ci?ncia e Tecnologia (INCT) do e-Universe (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Centre for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Centre for Cosmology and AstroParticle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovac¸ão, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the DES.

For ground-based optical imaging with current CCD technology, the Poisson fluctuations in source and sky background photon arrivals dominate the noise budget and are readily estimated. Another component of noise, however, is the signal from the undetected population of stars and galaxies. Using injection of artifical galaxies into images, we demonstrate that the measured variance of galaxy moments (used for weak gravitational lensing measurements) in Dark Energy Survey (DES) images is significantly in excess of the Poisson predictions, by up to 30 per cent, and that the background sky levels are overestimated by current software. By cross-correlating distinct images of 'empty' sky regions, we establish that there is a significant image noise contribution from undetected static sources (US), which, on average, are mildly resolved at DES resolution. Treating these US as a stationary noise source, we compute a correction to the moment covariance matrix expected from Poisson noise. The corrected covariance matrix matches the moment variances measured on the injected DES images to within 5 per cent. Thus, we have an empirical method to statistically account for US in weak lensing measurements, rather than requiring extremely deep sky simulations. We also find that local sky determinations can remove most of the bias in flux measurements, at a small penalty in additional, but quantifiable, noise., KDE and GMB acknowledge support from NASA through a Research Support Agreement with grant 12-EUCLID11-0004, and grants AST-1615555 from the US National Science Foundation, and DE-SC0007901 from the US Department of Energy. Funding for the DES Projects has been provided by the US Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample., 13 pages, 10 figures, the catalog with the ALHAMBRA classification is available at http://cosmohub.pic.es

We present a new method to measure the redshift-dependent galaxy bias by combining information from the galaxy density field and the weak lensing field. This method is based on Amara et al. (2012), where they use the galaxy density field to construct a bias-weighted convergence field kg. The main difference between Amara et al. (2012) and our new implementation is that here we present another way to measure galaxy bias using tomography instead of bias parameterizations. The correlation between kg and the true lensing field k allows us to measure galaxy bias using different zero-lag correlations, such as / or /. Our method measures the linear bias factor on linear scales under the assumption of no stochasticity between galaxies and matter. We use the MICE simulation to measure the linear galaxy bias for a flux-limited sample (i < 22.5) in tomographic redshift bins using this method. This paper is the first that studies the accuracy and systematic uncertainties associated with the implementation of the method, and the regime where it is consistent with the linear galaxy bias defined by projected 2-point correlation functions (2PCF). We find that our method is consistent with linear bias at the percent level for scales larger than 30 arcmin, while nonlinearities appear at smaller scales. This measurement is a good complement to other measurements of bias, since it does not depend strongly on sigma8 as the 2PCF measurements. We apply this method to the Dark Energy Survey Science Verification data in a follow-up paper., Comment: 15 pages, 9 figures

We present the scientific performance results of PynPoint, our Python-based software package that uses principle component analysis to detect and estimate the flux of exoplanets in two dimensional imaging data. Recent advances in adaptive optics and imaging technology at visible and infrared wavelengths have opened the door to direct detections of planetary companions to nearby stars, but image processing techniques have yet to be optimized. We show that the performance of our approach gives a marked improvement over what is presently possible using existing methods such as LOCI. To test our approach, we use real angular differential imaging (ADI) data taken with the adaptive optics assisted high resolution near-infrared camera NACO at the VLT. These data were taken during the commissioning of the apodising phase plate (APP) coronagraph. By inserting simulated planets into these data, we test the performance of our method as a function of planet brightness for different positions on the image. We find that in all cases PynPoint has a detection threshold that is superior to that given by our LOCI analysis when assessed in a common statistical framework. We obtain our best improvements for smaller inner working angles (IWA). For an IWA of ~ 0.29" we find that we achieve a detection sensitivity that is a factor of 5 better than LOCI. We also investigate our ability to correctly measure the flux of planets. Again, we find improvements over LOCI, with PynPoint giving more stable results. Finally, we apply our package to a non-APP dataset of the exoplanet beta Pictoris b and reveal the planet with high signal-to-noise. This confirms that PynPoint can potentially be applied with high fidelity to a wide range of high-contrast imaging datasets.

Weak lensing experiments are a powerful probe of cosmology through their measurement of the mass distribution of the universe. A challenge for this technique is to control systematic errors that occur when measuring the shapes of distant galaxies. In this paper we investigate noise bias, a systematic error that arises from second order noise terms in the shape measurement process. We first derive analytical expressions for the bias of general Maximum Likelihood Estimators (MLEs) in the presence of additive noise. We then find analytical expressions for a simplified toy model in which galaxies are modeled and fitted with a Gaussian with its size as a single free parameter. Even for this very simple case we find a significant effect. We also extend our analysis to a more realistic 6-parameter elliptical Gaussian model. We find that the noise bias is generically of the order of the inverse-squared signal-to-noise ratio (SNR) of the galaxies and is thus of the order of a percent for galaxies of SNR of 10, i.e. comparable to the weak lensing shear signal. This is nearly two orders of magnitude greater than the systematics requirements for future all-sky weak lensing surveys. We discuss possible ways to circumvent this effect, including a calibration method using simulations discussed in an associated paper.

The COSMOS field has been the subject of a wide range of observations, with a number of studies focusing on reconstructing the 3D dark matter density field. Typically, these studies have focused on one given method or tracer. In this paper, we reconstruct the distribution of mass in the COSMOS field out to a redshift z=1 by combining Hubble Space Telescope weak lensing measurements with zCOSMOS spectroscopic measurements of galaxy clustering. The distribution of galaxies traces the distribution of mass with high resolution (particularly in redshift, which is not possible with lensing), and the lensing data empirically calibrates the mass normalisation (bypassing the need for theoretical models). Two steps are needed to convert a galaxy survey into a density field. The first step is to create a smooth field from the galaxy positions, which is a point field. We investigate four possible methods for this: (i) Gaussian smoothing, (ii) convolution with truncated isothermal sphere, (iii) fifth nearest neighbour smoothing and (iv) a muliti-scale entropy method. The second step is to rescale this density field using a bias prescription. We calculate the optimal bias scaling for each method by comparing predictions from the smoothed density field with the measured weak lensing data, on a galaxy-by-galaxy basis. In general, we find scale-independent bias for all the smoothing schemes, to a precision of 10%. For the nearest neighbour smoothing case, we find the bias to be 2.51\pm 0.25. We also find evidence for a strongly evolving bias, increasing by a factor of ~3.5 between redshifts 0

We study the constraining power on primordial non-Gaussianity of future surveys of the large-scale structure of the Universe for both near-term surveys (such as the Dark Energy Survey - DES) as well as longer term projects such as Euclid and WFIRST. Specifically we perform a Fisher matrix analysis forecast for such surveys, using DES-like and Euclid-like configurations as examples, and take account of any expected photometric and spectroscopic data. We focus on two-point statistics and we consider three observables: the 3D galaxy power spectrum in redshift space, the angular galaxy power spectrum, and the projected weak-lensing shear power spectrum. We study the effects of adding a few extra parameters to the basic LCDM set. We include the two standard parameters to model the current value for the dark energy equation of state and its time derivative, w_0, w_a, and we account for the possibility of primordial non-Gaussianity of the local, equilateral and orthogonal types, of parameter fNL and, optionally, of spectral index n_fNL. We present forecasted constraints on these parameters using the different observational probes. We show that accounting for models that include primordial non-Gaussianity does not degrade the constraint on the standard LCDM set nor on the dark-energy equation of state. By combining the weak lensing data and the information on projected galaxy clustering, consistently including all two-point functions and their covariance, we find forecasted marginalised errors sigma (fNL) ~ 3, sigma (n_fNL) ~ 0.12 from a Euclid-like survey for the local shape of primordial non-Gaussianity, while the orthogonal and equilateral constraints are weakened for the galaxy clustering case, due to the weaker scale-dependence of the bias. In the lensing case, the constraints remain instead similar in all configurations.

The success of future large scale weak lensing surveys will critically depend on the accurate estimation of photometric redshifts of very large samples of galaxies. This in turn depends on both the quality of the photometric data and the photo-z estimators. In a previous study, (Bordoloi et al. 2010) we focussed primarily on the impact of photometric quality on photo-z estimates and on the development of novel techniques to construct the N(z) of tomographic bins at the high level of precision required for precision cosmology, as well as the correction of issues such as imprecise corrections for Galactic reddening. We used the same set of templates to generate the simulated photometry as were then used in the photo-z code, thereby removing any effects of "template error". In this work we now include the effects of "template error" by generating simulated photometric data set from actual COSMOS photometry. We use the trick of simulating redder photometry of galaxies at higher redshifts by using a bluer set of passbands on low z galaxies with known redshifts. We find that "template error" is a rather small factor in photo-z performance, at the photometric precision and filter complement expected for all-sky surveys. With only a small sub-set of training galaxies with spectroscopic redshifts, it is in principle possible to construct tomographic redshift bins whose mean redshift is known, from photo-z alone, to the required accuracy of 0.002(1+z).

Cosmic shear has been identified as the method with the most potential to constrain dark energy. To capitalise on this potential it is necessary to measure galaxy shapes with great accuracy, which in turn requires a detailed model for the image blurring, the Point Spread Function (PSF). In general the PSF varies with wavelength and therefore the PSF integrated over an observing filter depends on the spectrum of the object. For a typical galaxy the spectrum varies across the galaxy image, thus the PSF depends on the position within the image. We estimate the bias on the shear due to such colour gradients by modelling galaxies using two co-centered, co-elliptical Sersic profiles, each with a different spectrum. We estimate the effect of ignoring colour gradients and find the shear bias from a single galaxy can be very large depending on the properties of the galaxy. We find that halving the filter width reduces the shear bias by a factor of about 5. We show that, to first order, tomographic cosmic shear two point statistics depend on the mean shear bias over the galaxy population at a given redshift. For a single broad filter, and averaging over a small galaxy catalogue, we find a mean shear bias which is subdominant to the predicted statistical errors for future cosmic shear surveys. However, the true mean shear bias may exceed the statistical errors, depending on how accurately the catalogue represents the observed distribution of galaxies in the cosmic shear survey. We then investigate the bias on the shear for two-filter imaging and find that the bias is reduced by at least an order of magnitude. Lastly, we find that it is possible to calibrate galaxies for which colour gradients were ignored using two-filter imaging of a fair sample of noisy galaxies, if the galaxy model is known. For a S/N of 25 the number of galaxies required in each tomographic redshift bin is of order 1e4.

We investigate the statistics of flux anomalies in gravitationally lensed QSOs as a function of dark matter halo properties such as substructure content and halo ellipticity. We do this by creating a very large number of simulated lenses with finite source sizes to compare with the data. After analyzing these simulations, our conclusions are: 1) The finite size of the source is important. The point source approximation commonly used can cause biased results. 2) The widely used R_cusp statistic is sensitive to halo ellipticity as well as the lens' substructure content. 3) For compact substructure, we find new upper bounds on the amount of substructure from the the fact that no simple single-galaxy lenses have been observed with a single source having more than four well separated images. 4) The frequency of image flux anomalies is largely dependent on the total surface mass density in substructures and the size--mass relation for the substructures, and not on the range of substructure masses. 5) Substructure models with the same size--mass relation produce similar numbers of flux anomalies even when their internal mass profiles are different. 6) The lack of high image multiplicity lenses puts a limit on a combination of the substructures' size--mass relation, surface density and mass. 7) Substructures with shallower mass profiles and/or larger sizes produce less extra images. 8) The constraints that we are able to measure here with current data are roughly consistent with \LambdaCDM Nbody simulations.

Point Spread Function (PSF) modeling is a central part of any astronomy data analysis relying on measuring the shapes of objects. It is especially crucial for weak gravitational lensing, in order to beat down systematics and allow one to reach the full potential of weak lensing in measuring dark energy. A PSF modeling pipeline is made of two main steps: the first one is to assess its shape on stars, and the second is to interpolate it at any desired position (usually galaxies). We focus on the second part, and compare different interpolation schemes, including polynomial interpolation, radial basis functions, Delaunay triangulation and Kriging. For that purpose, we develop simulations of PSF fields, in which stars are built from a set of basis functions defined from a Principal Components Analysis of a real ground-based image. We find that Kriging gives the most reliable interpolation, significantly better than the traditionally used polynomial interpolation. We also note that although a Kriging interpolation on individual images is enough to control systematics at the level necessary for current weak lensing surveys, more elaborate techniques will have to be developed to reach future ambitious surveys' requirements.

This paper is aimed at developing a better understanding of the structure of the information that in contained in galaxy surveys, so as to find optimal ways to combine observables from such surveys. We first show how Jaynes' Maximal Entropy Principle allows us, in the general case, to express the Fisher information content of data sets in terms of the curvature of the Shannon entropy surface with respect to the relevant observables. This allows us to understand the Fisher information content of a data set, once a physical model is specified, independently of the specific way that the data will be processed, and without any assumptions of Gaussianity. This includes as a special case the standard Fisher matrix prescriptions for Gaussian variables widely used in the cosmological community, for instance for power spectra extraction. As an application of this approach, we evaluate the prospects of a joint analysis of weak lensing tracers up to second order in the shapes distortions, in the case that the noise in each probe can be effectively treated as model independent. These include the magnification, the two ellipticity and the four flexion fields. At the two point level, we show that the only effect of treating these observables in combination is a simple scale dependent decrease of the noise contaminating the accessible spectrum of the lensing E-mode. We provide simple bounds to its extraction by a combination of such probes, as well as its quantitative evaluation when the correlations between the noise variables for any two such probes can be neglected.

Given a standard model to test, an experiment can be designed to: (i) measure the standard model parameters; (ii) extend the standard model; or (iii) look for evidence of deviations from the standard model. To measure (or extend) the standard model, the Fisher matrix is widely used in cosmology to predict expected parameter errors for future surveys under Gaussian assumptions. In this article, we present a frame- work that can be used to design experiments such that it maximises the chance of finding a deviation from the standard model. Using a simple illustrative example, discussed in the appendix, we show that the optimal experimental configuration can depend dramatically on the optimisation approach chosen. We also show some simple cosmology calculations, where we study Baryonic Acoustic Oscillation and Supernove surveys. In doing so, we also show how external data, such as the positions of the CMB peaks measured by WMAP, and theory priors can be included in the analysis. In the cosmological cases that we have studied (DETF Stage III), we find that the three optimisation approaches yield similar results, which is reassuring and indicates that the choice of optimal experiment is fairly robust at this level. However, this may not be the case as we move to more ambitious future surveys.

It is of great interest to measure the properties of substructures in dark matter halos at galactic and cluster scales. Here we suggest a method to constrain substructure properties using the variance of weak gravitational flexion in a galaxy-galaxy lensing context. We show the effectiveness of flexion variance in measuring substructures in N-body simulations of dark matter halos, and present the expected galaxy-galaxy lensing signals. We show the insensitivity of the method to the overall galaxy halo mass, and predict the method's signal-to-noise for a space-based all-sky survey, showing that the presence of substructure down to 10^9 M_\odot halos can be reliably detected.

Cosmic shear measurements rely on our ability to measure and correct the point spread function (PSF) of the observations. This PSF is measured using stars in the field, which give a noisy measure at random points in the field. Using Wiener filtering, we show how errors in this PSF correction process propagate into shear power spectrum errors. This allows us to test future space-based missions, such as Euclid or the Joint Dark Energy Mission, thereby allowing us to set clear engineering specifications on PSF variability. For ground-based surveys, where the variability of the PSF is dominated by the environment, we briefly discuss how our approach can also be used to study the potential of mitigation techniques such as correlating galaxy shapes in different exposures. To illustrate our approach we show that for a Euclid-like survey to be statistics limited, an initial pre-correction PSF ellipticity power spectrum, with a power-law slope of -3, must have an amplitude of less than l 2 C ∈1 /2π < 3 x 10 -7 at l = 1000. This is 200 times smaller than the typical lensing signal at this scale. We also find that the power spectrum of the PSF size (δ R 2 ) at this scale must be below l 2 C R2 /2π < 2 x 10- 6 . The public code is available as part of ICOSMO at http://www.icosmo.org.

In the absence of any compelling physical model, cosmological systematics are often misrepresented as statistical effects and the approach of marginalising over extra nuisance systematic parameters is used to gauge the effect of the systematic. In this article we argue that such an approach is risky at best since the key choice of function can have a large effect on the resultant cosmological errors. As an alternative we present a functional form filling technique in which an unknown, residual, systematic is treated as such. Since the underlying function is unknown we evaluate the effect of every functional form allowed by the information available (either a hard boundary or some data). Using a simple toy model we introduce the formalism of functional form filling. We show that parameter errors can be dramatically affected by the choice of function in the case of marginalising over a systematic, but that in contrast the functional form filling approach is independent of the choice of basis set. We then apply the technique to cosmic shear shape measurement systematics and show that a shear calibration bias of |m(z)|< 0.001(1+z)^0.7 is required for a future all-sky photometric survey to yield unbiased cosmological parameter constraints to percent accuracy. A module associated with the work in this paper is available through the open source iCosmo code available at http://www.icosmo.org ., 24 pages, 18 figures, accepted to MNRAS

With increasingly large data sets, weak lensing measurements are able to measure cosmological parameters with ever greater precision. However this increased accuracy also places greater demands on the statistical tools used to extract the available information. To date, the majority of lensing analyses use the two point-statistics of the cosmic shear field. These can either be studied directly using the two-point correlation function, or in Fourier space, using the power spectrum. But analyzing weak lensing data inevitably involves the masking out of regions or example to remove bright stars from the field. Masking out the stars is common practice but the gaps in the data need proper handling. In this paper, we show how an inpainting technique allows us to properly fill in these gaps with only $N \log N$ operations, leading to a new image from which we can compute straight forwardly and with a very good accuracy both the pow er spectrum and the bispectrum. We propose then a new method to compute the bispectrum with a polar FFT algorithm, which has the main advantage of avoiding any interpolation in the Fourier domain. Finally we propose a new method for dark matter mass map reconstruction from shear observations which integrates this new inpainting concept. A range of examples based on 3D N-body simulations illustrates the results.

We describe a method for computing the biases that systematic signals introduce in parameter estimation using a simple extension of the Fisher matrix formalism. This allows us to calculate the offset of the best fit parameters relative to the fiducial model, in addition to the usual statistical error ellipse. As an application, we study the impact that residual systematics in tomographic weak lensing measurements. In particular we explore three different types of shape measurement systematics: (i) additive systematic with no redshift evolution; (ii) additive systematic with redshift evolution; and (iii) multiplicative systematic. In each case, we consider a wide range of scale dependence and redshift evolution of the systematics signal. For a future DUNE-like full sky survey, we find that, for cases with mild redshift evolution, the variance of the additive systematic signal should be kept below 10^-7 to ensure biases on cosmological parameters that are sub-dominant to the statistical errors. For the multiplicative systematics, which depends on the lensing signal, we find the multiplicative calibration m0 needs to be controlled to an accuracy better than 10^-3. We find that the impact of systematics can be underestimated if their assumes redshift dependence is too simplistic. We provide simple scaling relations to extend these requirements to any survey geometry and discuss the impact of our results for current and future weak lensing surveys.

We study in detail the photometric redshift requirements needed for tomographic weak gravitational lensing in order to measure accurately the Dark Energy equation of state. In particular, we examine how ground-based photometry (u,g,r,i,z,y) can be complemented by space-based near-infrared (IR) photometry (J,H), e.g. on board the planned DUNE satellite. Using realistic photometric redshift simulations and an artificial neural network photo-z method we evaluate the Figure of Merit for the Dark Energy parameters $(w_0, w_a)$. We consider a DUNE-like broad optical filter supplemented with ground-based multi-band optical data from surveys like the Dark Energy Survey, Pan-STARRS and LSST. We show that the Dark Energy Figure of Merit would improved by a factor of 1.3 to 1.7 if IR filters are added on board DUNE. Furthermore we show that with IR data catastrophic photo-z outliers can be removed effectively. There is an interplay between the choice of filters, the magnitude limits and the removal of outliers. We draw attention to the dependence of the results on the galaxy formation scenarios encoded into the mock galaxies, e.g the galaxy reddening. For example, deep u band data could be as effective as the IR. We also find that about $10^5-10^6$ spectroscopic redshifts are needed for calibration of the full survey.

Weak lensing surveys provide a powerful probe of dark energy through the measurement of the mass distribution of the local Universe. A number of ground-based and space-based surveys are being planned for this purpose. Here, we study the optimal strategy for these future surveys using the joint constraints on the equation of state parameter wn and its evolution wa as a figure of merit by considering power spectrum tomography. For this purpose, we first consider an `ideal' survey which is both wide and deep and exempt from systematics. We find that such a survey has great potential for dark energy studies, reaching one sigma precisions of 1% and 10% on the two parameters respectively. We then study the relative impact of various limitations by degrading this ideal survey. In particular, we consider the effect of sky coverage, survey depth, shape measurements systematics, photometric redshifts systematics and uncertainties in the non-linear power spectrum predictions. We find that, for a given observing time, it is always advantageous to choose a wide rather than a deep survey geometry. We also find that the dark energy constraints from power spectrum tomography are robust to photometric redshift errors and catastrophic failures, if a spectroscopic calibration sample of 10^4-10^5 galaxies is available. The impact of these systematics is small compared to the limitations that come from potential uncertainties in the power spectrum, due to shear measurement and theoretical errors. To help the planning of future surveys, we summarize our results with comprehensive scaling relations which avoid the need for full Fisher matrix calculations.

The COSMOS field has been the subject of a wide range of observations, with a number of studies focusing on reconstructing the 3D dark matter density field. Typically, these studies have focused on one given method or tracer. In this paper, we reconstruct the distribution of mass in the COSMOS field out to a redshift z=1 by combining Hubble Space Telescope weak lensing measurements with zCOSMOS spectroscopic measurements of galaxy clustering. The distribution of galaxies traces the distribution of mass with high resolution (particularly in redshift, which is not possible with lensing), and the lensing data empirically calibrates the mass normalisation (bypassing the need for theoretical models). Two steps are needed to convert a galaxy survey into a density field. The first step is to create a smooth field from the galaxy positions, which is a point field. We investigate four possible methods for this: (i) Gaussian smoothing, (ii) convolution with truncated isothermal sphere, (iii) fifth nearest neighbour smoothing and (iv) a muliti-scale entropy method. The second step is to rescale this density field using a bias prescription. We calculate the optimal bias scaling for each method by comparing predictions from the smoothed density field with the measured weak lensing data, on a galaxy-by-galaxy basis. In general, we find scale-independent bias for all the smoothing schemes, to a precision of 10%. For the nearest neighbour smoothing case, we find the bias to be 2.51\pm 0.25. We also find evidence for a strongly evolving bias, increasing by a factor of ~3.5 between redshifts 0, Comment: Accepted by MNRAS. 12 pages with 6 Figures