text:
\section{Introduction}
The dawn of ``precision cosmology'' was heralded by the results of surveys which, for the first time, produced data of sufficient quantity and quality that our cosmological probes could begin to accurately measure some of the fundamental properties of the Universe. The outcome has been the $\Lambda$CDM concordance cosmology, a description of the Universe compatible with the joint constraints from many sources of cosmological information.
The next decades will see a step-change in our ability to measure cosmological parameters as new surveys produce orders of magnitude more data than has previously been available. This will allow us to test the standard cosmological model as never before.

The standard $\Lambda$CDM model describes a Universe made up of $\sim$75\% Dark Energy, a smooth, negative pressure fluid, $\sim$20\% Dark Matter, collisionless massive particles which interact solely via gravity, and just $\sim$5\% baryons making up the
potentially visible mass of the Universe \citep{dunkleyetal}. The standard model also assumes gravity to be described by Einstein's General Relativity (GR).

A Universe governed by GR and populated by standard gravitating matter cannot explain the observed acceleration. The dark energy fluid was proposed to solve this paradox, reviving the idea of a cosmological constant which Einstein had originally included in his GR field equations and subsequently discarded. Much effort has been devoted to characterising the nature of dark energy- particularly attempts to discriminate between a pure cosmological constant and a dynamic scalar potential with time-varying equation of state $w(a)$ \citep{detf}. In all its forms, dark energy poses problems of fine-tuning for which we have, as yet, no physical motivation.

Rather than invoke dark energy, an alternative explanation for cosmic acceleration has been proposed -- that our standard theory of gravity is incomplete and that a correct theory of gravity would explain cosmic acceleration at late times and large scales in a universe populated by  matter with positive pressure. There are a large number of theoretically motivated modified theories of gravity, see \citet{jain_khoury} for a review.
Testing the theory of gravity on cosmic scales will be one of the most interesting opportunities afforded by upcoming survey data.

Weak gravitational lensing (WL)  is a particularly useful probe of gravity because it is sensitive to $\phi + \psi$, the sum of the metric potentials. Through the modified growth of structure and geometry via the lensing integral cosmic shear can constrain both
the ratio of metric potentials and modifications to the Poisson equation.
In contrast probes such as galaxy redshift surveys and galaxy peculiar velocities depend only on the Newtonian potential, $\psi$.
WL constraints also have the benefit that they probe the dark matter distribution directly, avoiding the impact of galaxy biasing. Joint constraints from combining multiple probes can help break degeneracies between
free parameters, producing the tightest constraints.

Cosmic shear is the name given to weak gravitational lensing in random patches of the sky and was first detected a decade ago \citep{Kaiser:2000if,Wittman:2000tc,van_Waerbeke:2000rm,Bacon:2000yp}.
The latest constraints on cosmology come from the Hubble Space Telescope COSMOS survey \citep{schrabbackea09,massey_growth2007_mnras}, and the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) \citep{fuea08_mnras}. In addition the 100 square degree survey \citep{benjaminea07} combines data from several smaller surveys \citep{hoekstraea2002RCS, hetterscheidt2007, lefevreea04, hoekstraea2002RCS}.

Cosmic shear has been identified as the method with the most potential to uncover the nature of dark energy \citep{detf,Peacock:2006kj} and therefore a number of surveys are planned with a major cosmic shear component. Upcoming ``Stage III" projects include Kilo-degree Survey (KIDS) on the Very Large Telescope (VLT) Survey Telescope (VST), the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) project, the Subaru Meaurement of Images and Redshifts (HSC) survey using HyperSUPRIMECam, the Dark Energy Survey (DES) on the Blanco Telescope. More ambitious ``Stage IV"
imaging projects are the Large Synoptic Survey Telescope (LSST) ground-based project and in space the proposed European Space Agency mission Euclid and the NASA proposed Wide-Field Infrared Survey Telescope (WFIRST).

Greater accuracy demands better treatment of systematic effects. One of the main systematics in cosmic shear studies is the contamination by the intrinsic alignment
(IA) of galaxy shapes. This systematic manifests in two main ways, in the first physically close galaxies have preferentially aligned ellipticities, this is known as the Intrinsic-Intrinsic (II) correlation. In the second galaxies close on the sky by separated radially are anti-correlated as the foreground gravitational potential shapes the closer galaxy while also gravitationally lensing the background galaxy, this is called the Gravitational-Intrinsic (GI) correlation. The former adds to the cosmic shear signal we wish to measure, the latter subtracts from it.
 In an earlier paper \citep{MGPaper1}, herein ``LBKB", we established how, for a fixed survey specification, the inclusion of a realistic IA model and the inherent uncertainties in the model significantly degrade the constraining power of a cosmic shear survey for dark energy and modified gravity cosmological models. This work deepened the conclusions of \citet{bernstein_2008,joachimi_bridle_2009} and expanded them into the MG domain.

What is encouraging, however, is that the use of position-position correlations, $nn$, and particularly position-shear, $n\epsilon$, cross-correlations can go some way towards mitigating the impact of IAs. This data is already collected by a standard WL survey. In the presence of IAs, constraints from $\epsilon\epsilon + n\epsilon + nn$ are roughly twice as strong as those from $\epsilon\epsilon$ alone. Until now, the discussion of optimal survey parameters has generally assumed that $\epsilon\epsilon$ correlations alone will be included, without IAs. In this paper we investigate the impact of survey strategy and design on different combinations of probes, with and without IAs. This is a continuation of the cosmic shear survey optimisation work begun by, among others, \citet{amarar07,bridleandking,joachimi_bridle_2009,mahh06,Huterer:2005ez,huterertbj06}

In this paper we expand on the work on LBKB to fully understand how the observing strategy of a given survey is central to the type of data and the quality of results that survey will produce. Different survey geometries with the same instrument will produce different statistical errors due to a different balance between survey characteristics, the most important for cosmic shear being survey area galaxy number density on the sky
and median redshift of the galaxy distribution.
Other properties such as required redshift accuracy are important guidelines for instrument designers and those preparing analysis pipelines and follow-up or calibration studies \citep{amarar07}.  While more area and increased number density will always be desirable, it is important to keep firm goals in mind when producing desiderata for future surveys which will always be limited by technology and finite observing time. In particular learning about dark energy and modified gravity may benefit from different survey strategies and call for the prioritisation of different properties.

The paper is organised as follows: in section \ref{sec:setup}, we summarise our standard cosmology, fiducial surveys and models for deviations from GR, Intrinsic Alignments and bias parameterisation, as well as our figures of merit for DE and MG. In section \ref{sec:discussion} we investigate the sensitivity of dark energy and modified gravity constraints to changes in survey specification. In section \ref{sec:area} we present the effect of varying survey area and the impact of finite survey time on overall strategy and the ability to constrain MG. The importance of photometric redshift accuracy is addressed in sections \ref{sec:photo-zs} and \ref{sec:zpriors}.
 Conclusions are made in section \ref{sec:conclusions}.

\section{Cosmological Set-Up}
\label{sec:setup}

The paper deals with the impact of various aspects of survey strategy and redshift quality on the power of cosmic shear and galaxy position information to constrain dark energy and deviations from General Relativity. In ~\ref{sec:setup-cosmicshear} we summarise our cosmic shear formalism, ~\ref{sec:setup-IAs} extends it to include intrinsic alignments and ~\ref{sec:setup-galaxys} adds galaxy position auto- and cross-correlations and introduces a coherent biasing formalism for IAs and galaxy bias. ~\ref{sec:setup-MG} reviews the MG parameterisation we use and how it enters our angular power spectrum integrals.
In ~\ref{sec:setup-cosmology} we summarise some basic cosmology we use throughout the paper and ~\ref{sec:setup-surveys} describes the fiducial survey parameters we use in the following sections.

\subsection{Cosmic Shear}
\label{sec:setup-cosmicshear}
Cosmic shear is the shape-distortion induced in the image of a distant galaxy due to the bending of its light by gravity as it passes massive structure in the Universe.
If we assume General Relativity holds then we can define the cosmic shear angular power spectrum under the Limber approximation as
\begin{equation}
C_{ij}^{GG}(l) = \int \frac{d\chi}{\chi^2} W_{i}(\chi)W_{j}(\chi) P_{\delta\delta}(k,z)
\label{eqn:GG_integral}
\end{equation}
where $P_{\delta\delta}(k,z)$ is the three dimensional matter power spectrum, $\chi$ is the comoving distance in units of $h^{-1}$Mpc and $W(\chi)$ is the lensing efficiency function
\begin{equation}
W_{i}(\chi) = \frac{4\pi G}{c^2}\rho(z)a^{2}(z)\chi \int d\chi' n_{i}(\chi')\frac{\chi'-\chi}{\chi'}
\end{equation}

\subsection{Intrinsic Alignments}
\label{sec:setup-IAs}
The intrinsic alignment (IA) of galaxy ellipticities is a prime contaminant to the measured cosmic shear signal. A naive approach to cosmic shear assumes that galaxy's intrinsic ellipticities are randomly distributed on the sky so, when we average over observed ellipticity in a small patch, intrinsic ellipticity cancels and we are left with the induced shear.

Unfortunately, this assumption is invalid because galaxy ellipticities are aligned due to two effects
arising from the same physical intrinsic alignment origin. Physically close galaxies tend to align with the local gravitational tidal field and so are positively correlated with each other, this is the Intrinsic-Intrinsic (II) alignment. Background galaxies can have their light lensed by foreground gravitational fields which align the intrinsic ellipticity of foreground galaxies. This induces an anti-correlation and is the gravitational-intrinsic (GI) alignment.

We follow the procedure of LBKB and implement IAs using an updated version of the corrected LA model of \citet{hiratas04}, incorporating their correction of \emph{erratum 2010} and assuming that all IA physics occurs at a high-z epoch of galaxy formation hence the IA signal depends only on the linear matter power spectrum rather than the subsequent nonlinear evolution. The rest of the LA model enters as a factor of
\begin{equation}
b_I = -C_{1}\rho_{m}(z=0)
\label{Isource}
\end{equation}
where $\rho_m(z=0)$ is the matter density today and  $C_1 =\textrm 5\times10^{-14}(h^{2}M_{\odot}Mpc^{-3})^{-1}$ is the amplitude of the IA term, normalised to redshift zero \citep{bridleandking}. This factor appears in the IA projected angular power spectra, linearly in the Gravitational-Intrinsic (GI) correlation and squared for the Intrinsic-Intrinsic (II). The window function associated with IAs is the galaxy redshift distribution, $n(z)$, see table \ref{table:Cls} for the full angular power spectra equations.

In section \ref{sec:setup-galaxys} we expand our notation to allow nuisance parameters to parameterise our knowledge of IAs, galaxy bias and their cross-correlations. In this more general case $b_I$ given in eqn. \ref{Isource} becomes the fiducial value of a variable IA term which appears quadratically in the II power spectrum and linearly in the GI and gI power spectra.

The total observed lensing signal is then the sum of the cosmic shear and the IA terms
\begin{equation}
C_{l}^{\epsilon\epsilon} =C_{l}^{GG} + C_{l}^{II} + C_{l}^{GI}.
\end{equation}

\subsection{Galaxy Position Data}
\label{sec:setup-galaxys}

\begin{table*}
\caption{Summary of the projected angular power spectra considered in this work.}
\centering
\begin{tabular}[t]{l||l}
Correlation & 2D PS \\
\hline
\hline
Shear                                   & $C_{ij}^{GG}(l) = \int \frac{d\chi}{\chi^2}W_{i}(\chi)W_{j}(\chi)\left[ Q(z)\frac{1+R(z)}{2} \right] ^2 P_{\delta\delta}(k,z) $ \\
Intrinsic-shear                         & $C_{ij}^{GI}(l) = \int \frac{d\chi}{\chi^2}W_{i}(\chi)n_{j}(\chi)Q(z)Q(z_f)R(z_f)(1+\frac{R(z)}{2})b_{I}(k,z)r_{I}(k,z)\frac{\sqrt{P_{\delta\delta}(k,z_f)P_{\delta\delta}(k,z)}}{D(z_f)}$\\
Intrinsic                               & $C_{ij}^{II}(l) = \int \frac{d\chi}{\chi^2}n_{i}(\chi)n_{j}(\chi)b_{I}^{2}(k,z)Q^{2}(z_f)R^{2}(z_f) P_{\delta\delta}(k,z) $\\
Galaxy clustering                       & $C_{ij}^{gg}(l) = \int \frac{d\chi}{\chi^2}n_{i}(\chi)n_{j}(\chi)b_{g}^{2}(k,z) P_{\delta\delta}(k,z)$\\
Clustering-shear                        & $C_{ij}^{gG}(l) = \int \frac{d\chi}{\chi^2}n_{i}(\chi)W_{j}(\chi) Q(z)\frac{1+R(z)}{2}b_{g}(k,z)r_{g}(k,z) P_{\delta\delta}(k,z)$\\
Clustering-intrinsic                    & $C_{ij}^{gI}(l) = \int\frac{d\chi}{\chi^2}n_{i}(\chi)n_{j}(\chi)Q(z_f)R(z_f)b_{g}(k,z)b_{I}(k,z)r_{g}(k,z)r_{I}(k,z) \frac{\sqrt{P_{\delta\delta}(k,z_f)P_{\delta\delta}(k,z)}}{D(z_f)}$\\
Galaxy ellipticity (observable)         & $C_{ij}^{\epsilon \epsilon} =  C_{ij}^{GG}+ C_{ij}^{II}+ C_{ij}^{GI}$  \\
Galaxy number density (observable)      & $C_{ij}^{n n} = C_{ij}^{gg}$          \\
Number density-ellipticity (observable) & $C_{ij}^{n \epsilon} =  C_{ij}^{gI} +  C_{ij}^{gG}$          \\
\end{tabular}
\label{table:Cls}
\end{table*}

A cosmic shear survey contains galaxy position information (angular position on the sky and redshift) as well as measurements of galaxy shear. \citet{joachimi_bridle_2009} provide a formalism for including this additional information in cosmological parameter constraints and show how this extra information can serve to partially mitigate the impact of IAs. This approach follows from the work of \citet{zhang08,hu_jain_2004}

The extra observables we use are galaxy position-position power spectra and the position-shear cross-spectra, defined analogously to the shear-shear power spectrum:
\begin{align}
C_{l}^{nn} &= C_{l}^{gg} \\
C_{l}^{n\epsilon} &= C_{l}^{gI} + C_{l}^{gG}.
\end{align}
We now assign nuisance parameters to each component $C_l$ in a self-consistent way as explained in LBKB. The four bias
function are $b_g$, $b_I$, $r_g$ and $r_I$. $b_g$ and $b_I$ model bias from galaxy position and IAs amplitudes, and their cross-correlations are modelled by $r_g$ and $r_I$. This unified approach was first introduced by \citet{bernstein_2008}.
Table \ref{table:Cls} summarises the full power spectra equations, consistent with the definitions in LBKB eqns. 37, 38, 43 and 44.

We let each bias parameter vary in amplitude and as a function of scale
and redshift using a $N_k \times N_z$
grid of free parameters interpolated over
$k$-,$z$-space, i.e. each nuisance factor, $b_X = A_x Q_{X}(k,z)$ is the product of a variable constant amplitude parameter, $A_X$, and a variable grid, $Q_{X}(k,z)$ in $k$ and $z$.  Throughout this paper the grid size is set to the fiducial value of $N_k = N_z = 5$, which means marginalisation over 104 nuisance parameters when the full $\epsilon\epsilon+n\epsilon+nn$ probe combination is considered in the presence of IAs.
The nuisance parameters multiply linearly into the angular power spectra integrands and each power spectrum depends on a subset of the bias parameters as follows:
\begin{equation}
\begin{array}{cccc}
C_{l}^{GG}: & -\phantom{a} ,& C_{l}^{gG}: & b_{g}r_{g} \\
C_{l}^{II}: & b_{I}b_{I}, & C_{l}^{gI}: & b_{g}b_{I}r_{g}r_{I} \\
C_{l}^{GI}: & b_{I}r_{I}, & C_{l}^{gg}: & b_{g}b_{g}
\end{array}
\end{equation}
As mentioned in the previous section, the fiducial value of $b_I$ is given by eqn. \ref{Isource}. $b_g$ has fiducial value 1 while $r_g$ and $r_I$ vary around the fiducial value 0.9 to avoid more than perfect cross-correlation.

We have ignored the effect of lensing magnification and followed LBKB and \citet{joachimi_bridle_2009} in applying a cut on multipole $l$ to any redshift bin combination $ij$ which includes galaxy position information (i.e. $nn$ or $n\epsilon$) according to $\ell_{max}(i)=k_{lin}^{max}(z_{med}^{(i)})\chi(z_{med}^{(i)})$. This aims to account for uncertainties in the galaxy bias model at small scales.
For $n\epsilon$ bin pairs, the cut is made on the galaxy, i.e. $n$, bin. For position-position, $nn$, pairs there is a choice of bin on which to apply the cut. We follow \citet{joachimi_bridle_2009} in making the optimistic choice and cutting on the higher redshift bin. Shear-shear, $\epsilon\epsilon$, pairs do not depend on galaxy bias and are therefore used up to the full range in $l$.

\subsection{Modified Gravity}
\label{sec:setup-MG}

There are a large number of modifications or extensions of Einstein's general theory of relativity
on cosmic scales which come under the general heading of Modified Gravity theories. These can be motivated by the presence of extra dimensions, as in DGP, or extra degrees of freedom compared to the GR action equation, as in f(R), TeVeS etc. \citep{DGP, carrollea_fR, skordis_teves, jain_khoury}. As in LBKB, we do not assume a particular modified theory of gravity but rather concentrate on ``trigger parameters'' whose deviation from their GR values would indicate the presence of some physics beyond that in the standard GR picture.

In the conformal newtonian gauge the metric for a flat FRW spacetime is written
\begin{equation}
ds^2 = -a(\tau)^2 \left[ 1 + 2\psi(x,t)\right]d\tau^2 + a(\tau)^2\left[1-2\phi(x,t) \right]dx^2
\end{equation}
where $\psi$ and $\phi$ are the scalar potentials which describe perturbations to the time- and space-parts of the metric respectively. We parameterise deviations from GR through two parameters, $Q$ and $R$. One alters the way the Newtonian potential responds to mass via the Poisson equation,
\begin{equation}
k^2\psi(x,t) = -4\pi GQ \rho a^2 \delta
\end{equation}
and the other modifies the ratio of the metric potentials
\begin{equation}
\psi(x,t) = R\phi(x,t)
\end{equation}
$Q$ and $R$  are assumed to be scale-independent and vary with redshift as
\begin{align}
Q &= (Q_0-1) a^s \\
R &= (R_0-1) a^s
\end{align}
where a is the scale-factor and $Q_0$,$R_0$ are the free parameters we vary for MG, and $s=3$. We are interested in MG theories which explain the acceleratin expansion of the Universe observed at late times. $s=3$ allows any modification to ``turn-on'' and late times and avoids violating early Universe constraints from the Cosmic Microwave Background (CMB) and Big Bang Nucleosynthesis (BBN).

The modified gravity parameters enter the projected angular power spectra in different combinations, as shown in Table \ref{table:Cls}, due to their different dependencies on the mater density field. As well as modifying the angular power spectra, Q and R enter the angular power spectrum integrals via their response to the metric potentials through the linear growth function. The potential for weak lensing to constrain deviations from GR has previously been noted in \citet{Bean:2010zq,Laszlo:2007td,Beynon:2009yd}.

The full projected angular power spectra, including IAs, MG and bias parameters, is summarised in Table \ref{table:Cls}.
Note also that deviations from GR will also manifest in the growth function which produces the matter power spectra in Table \ref{table:Cls}. We include modifications to growth via a ratio of MG/GR power spectra calculated using
the modified version of CAMB used for LBKB.
For ease, LBKB also provide a fitting function to compute this ratio over a range of $Q_0$, $R_0$ and $s$ values.

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|}
\hline
\multicolumn{3}{|c|}{Fiducial Survey Paramters} \\
\hline
Parameter & Stage III & Stage IV \\
\hline
Area            & 5,000deg$^2$ & 20,000deg$^2$ \\
$n_g$           & 10            & 35 \\
$\sigma_\gamma$ & 0.23          & 0.35 \\
$N_z$           & 5             & 10 \\
$\delta_z$      & 0.07          & 0.05 \\
$f_{cat}$       & 0             & 0 \\
$\Delta_z$      & 1             & 1 \\
$z_0$           & 0.8/1.412     & 0.9/1.412 \\
$\alpha$        & 2             & 2 \\
$\beta$         & 1.5           & 1.5 \\
\hline
\end{tabular}
\caption{Fiducial survey parameter values for the DES-like (stage III) and Euclid-like (stage IV) surveys.}
\label{fig:DESEuclid_table}
\end{center}
\end{table}

\subsection{General Cosmology}
\label{sec:setup-cosmology}

Throughout this paper we assume a flat $\Lambda$CDM cosmological model with fiducial parameter values equal to the WMAP7 best-fit values:
$\Omega_m = 0.262$, $\Omega_b = 0.0445$, $w_0 = -1$, $w_a = 0$, $\sigma_8 = 0.802$, $h = 0.714$, $n_s =0.969$,  $\Omega_\nu = 0$.
Where $\Omega_m$,$\Omega_b$ and $\Omega_\nu$ are the dimensionless matter, baryon and neutrino densities respectively, $w_0$ and $w_a$ are the dark energy equation of state parameters \citep{detf},$\sigma_8$ is the normalisation of the linear matter power spectrum, $h$ is the dimensionless Hubble parameter today and $n_s$ is the power law of the primordial power spectrum.
The linear matter power spectrum is given by the fitting formula of \citet{eisensteinhu97}, with nonlinear corrections  from \citet{smithea03}.

When we treat MG we assume that the background expansion of the Universe is consistent with our fiducial
dark energy model and deviations due to non-GR physics enter through the growth of structure from initial perturbations. Unless otherwise specified all plots include CMB priors from a Planck-type survey
as described in LBKB. We divide our survey galaxy redshift distributions into redshift slices with equal galaxy number density to allow redshift tomography where auto- and cross-correlations of these redshift bins are considered, allowing us to measure redshift evolution of the observables.

We calculate constraints on cosmological parameters using the Fisher Matrix formalism
\begin{equation}
F_{\mu\nu} = \sum_{m,n}^{N_d} \sum_{l}^{N_{l}^{max}}\frac{\partial\mathscr{D}_{m}(l)}{\partial p_{\mu}}\textrm{Cov}_{mn}^{-1}(l)\frac{\partial\mathscr{D}_{n}(l)}{\partial p_{\nu}}
\end{equation}
where $m$, $n$ label tomographic redshift bins,  $\mathscr{D}_{m}(l)$ is the data vector, in our case some combination of the angular power spectra $C_{l}^{\epsilon\epsilon}$, $C_{l}^{n\epsilon}$ and $C_{l}^{nn}$. $\textrm{Cov}_{mn}(l)$ is the covariance matrix, defined as in \citet{joachimi_bridle_2009}, $p_{\mu}$ are the parameters varied and $N_d$ is the number of independent combinations of tomographic bins. Unless otherwise stated the Fisher matrix varies the cosmological  parameters: $p = \{\Omega_m,w_0,w_a,h,\sigma_8,\Omega_b,n_s\}$.

Using the Fisher matrix formalism the lower limit on the minimum variance bound on the error of a parameter $p_\mu$
marginalised over all other parameters of interest, is given by $\sigma(p_\mu) = \sqrt{(F^{-1})_{\mu\mu}}$.
We quote results in terms of the DETF FoM for Dark Energy
\begin{equation}
FoM_{DE} = \frac{1}{\sqrt{\textrm{det}\left[(F^{-1}_{GR})_{w_{0},w_{a}}\right]}}.\label{DEFoM}
\end{equation}
Here $(F^{-1}_{GR})_{w_{0},w_{a}}$
is the $2\times 2$ submatrix of the inverted Fisher matrix for cosmological and nuisance parameters excluding the MG parameters are excluded (these are fixed at their GR values).  The FoM is proportional to the inverse of the area of the constraint contour in $w_0 - w_a$ space.

By analogy we define a MG FoM as
\begin{equation}
FoM_{MG} = \frac{1}{\sqrt{\textrm{det}\left[(F^{-1})_{Q_{0},Q_{0}(1+R_{0}/2)}\right]}}.
\label{MGFoM}
\end{equation}
where we have inverted the full fisher matrix including the modified gravity parameters
and dark energy parameters.

\subsection{General Survey Parameters}
\label{sec:setup-surveys}
LBKB investigated the degeneracy between IAs and MG parameters for a fixed survey specification. It was found that the inclusion of a realistic IA model reduced the FoM (dark energy or MG) for a typical stage-IV cosmic shear survey by $\sim$70\%. The effect could be substantially mitigated by the inclusion of galaxy position data and galaxy-shear cross-correlations. The constraining power of $\epsilon\epsilon+n\epsilon+nn$, including IAs, is roughly double that of $\epsilon\epsilon$, including IAs, alone. We showed that our results were robust to different numbers of free nuisance parameters, accounting for flexibility in the galaxy clustering and IA bias models. The results were broadly similar for attempts to constrain dark energy equation of state parameters and deviations from GR.

In this paper we vary certain survey parameters around a fiducial cosmic shear survey set-up, corresponding to
Stage IV project. This survey has an area = 20000deg$^2$,  number density of galaxies projected onto the sky, $n_g = 35$ arcmin$^{-2}$, and galaxy intrinsic ellipticity dispersion, $\sigma_\gamma = 0.35$, a Gaussian photometric redshift scatter of width $\delta_z = 0.05$ which is related to the (redshift dependent) rms photometric dispersion via $\sigma_z(z) = (1+z)\delta_z$, with a fraction of catastrophic outliers, $f_{\textrm{cat}} = 0$. We analyse the results using cosmic shear tomography with 10 tomographic redshift bins of equal number density.

The redshift distribution of galaxes, $n(z)$ is
assumed to be given by a Smail-type distribution \citep{smailef94}
\begin{equation}
\label{eq:smail}
n(z) \propto z^\alpha exp \left( -\frac{z}{z_0} \right)^{\beta}
\end{equation}
with $\alpha = 2$, $\beta = 1.5$ and $z_0 = z_{m}/1.412$, where the median redshift, $z_m$ = 0.9.

For comparison purposes, some results are also presented for the stage-III Dark Energy Survey (DES), due to see first light in early 2012. Table \ref{fig:DESEuclid_table}  summarises the specifications for both surveys.

\begin{figure*}
  \begin{flushleft}
    \centering
       \includegraphics[width=4.5in,height=4.5in]{fig_varyA_wCMB_edit.ps}
\caption{Figures showing the variation with survey area of a relative Stage IV survey FoM for dark energy (DE) parameters $w_0,w_a$ [blue
 lines], and modified gravity (MG) parameters $Q_0, Q_0(1+R_0)/2$ [red lines], as described in (\ref{DEFoM}) and (\ref{MGFoM}), relative to a `baseline' FoM.  The baseline includes shear  auto-correlations `GG' alone, excluding IAs, over a 20,000 square degree survey. All FoMs contain priors from a Planck-like CMB survey.
 The figures show  relative FoMs for an optimistic scenario, in which uncertainities in the IA model have been excluded, [top panels] and a conservative scenario, in which uncertainties in the IA model are marginalized over using a $N_k=N_z=5$ gridded bias model [lower panels]. The baseline model (relative FoM=1) is shown as a black cross in the top panels. We consider the impact on the relative FoM of increasing survey area, by increasing survey time for fixed limiting magnitude [left panels] and fixing the survey time to trade-off survey area and depth [right panels].  Four data combinations are considered in each panel, shear-shear correlations `$\epsilon\epsilon$' [dotted lines], galaxy-galaxy positions `$nn$' alone [dot-dash], '$\epsilon\epsilon$+$nn$' [dashed] and when shear-position cross-correlations `$n\epsilon$' are included [full lines]. For the galaxy position correlations a $N_k=N_z=5$ gridded bias model is used throughout. }
\label{fig:varyA}
  \end{flushleft}
\end{figure*}

 \begin{figure*}
 \center
       \includegraphics[width=4.5in,height=4.5in]{fig_Aconstt_DES_wCMB_edit.ps}
 \caption{
 As in figure \ref{fig:varyA} but for a Stage-III large scale structure survey with fiducial survey area of 5000 square degrees.}
 \label{fig:Aconstt_DES}
 \end{figure*}

\section{Survey specifications and forecast constraints}
\label{sec:discussion}
In this section we analyse the sensitivity of the prospective dark energy and modified gravity figures of merit on variations in survey specification, principally focusing on their relationship to the survey area and depth, and the precision of the photometric redshift measurements when uncertainties in how intrinsic alignments are included in the modeling.

Throughout, we  quantify constraints using the dark energy (DE) and modified gravity (MG) figures of merit in (\ref{DEFoM}) and (\ref{MGFoM}) normalised relative to a `baseline' figure of merit for the shear-only auto-correlations `$GG$', with IAs excluded, using the fiducial survey described in Table \ref{fig:DESEuclid_table}.

\subsection{Survey area}
\label{sec:area}

Figs.~\ref{fig:varyA} and \ref{fig:Aconstt_DES} present the key results showing how the constraining power of a survey, for dark energy equation of state and modified gravity parameters, varies with the survey area. We consider two alternative approaches, firstly a simple option where the increased area derives from an increase in survey time, and secondly a more realistic option when survey time is fixed, and increased survey area is achieved by a reduction in survey depth/ limiting flux.

Let us first consider the case where all survey parameters are kept fixed except for the survey area. We focus on the quantitative implications for the Stage IV survey, the results for a Stage III survey, presented in figure \ref{fig:Aconstt_DES} are qualitatively similar.

When IAs are excluded from the analysis the improvement in FoM with increasing area can be roughly described by a power law dependence FoM$ \propto$Area$^x$ with $x=0.4$ and 0.7  for CMB+shear correlations alone for the DE and MG FoMs. The MG FoM is slightly more sensitive to changes in area than the DE FoM. The DE index is lower than  that  reported in \citet{amarar07} only because of the inclusion of, survey area independent, CMB priors.

As discussed in LBKB, including IAs in the analysis deteriorates both the DE and MG FoMs, with the MG FoM more negatively impacted. For the fiducial Stage IV survey the FoMs are reduced by 60 and 65 \% relative to when IAs are excluded, consistent with LBKB. When IAs and their associated uncertainties  are marginalized over, the power law scaling is weaker, with $x=0.35$ for DE, so that the relative degradation of the FoM becomes more pronounced as survey area increases. This reflects that the IAs are astrophysical systematics that are not significantly removed by increasing the survey area.

Because of the uncertainties in the galaxy bias, through the gridded bias and cross-correlation coefficients, $b_g$ and $r_g$, the cosmological information from the galaxy position data is massively suppressed. For the fiducial full survey area FoMs for CMB+nn are 22 and 1\% of the CMB+pure lensing signal, for the scenarios with and without IAs respectively.

 If one adds in galaxy position autocorrelations to shear autocorrelations, `$nn+\epsilon\epsilon$', we find a 12 and 36 \%  improvement in the DE and with and without IAs respectively, and 19 and 40\% for MG FoMs. While the cross-correlations, `$nn+n\epsilon+\epsilon\epsilon$', give only a small improvement when IAs are excluded, they provide an 34\% improvement on `$nn+\epsilon\epsilon$' when uncertainties in the IAs are included. This results from the $n\epsilon$ correlations breaking the degeneracies between the cosmological and IA amplitude nuisance parameters.

In reality most surveys have a fixed observing time.
This inevitably leads to a trade off between total survey area and survey depth, which impacts both number density of lensed galaxies, $n_g$, and median redshift achieved, $z_m$.
To implement the fixed survey time option, we use a fitting formula for the number of galaxies per unit redshift per square arcminute
\begin{equation}
n(z) = \Sigma_0 \times \frac{3 z^2}{2 z_0^3} \exp{ \left[ -
\left( \frac{z}{z_0} \right)^{3/2} \right] }
\label{eqdndz}
\end{equation}
(e.g. \citet{baugh_efstathiou})
and we interpolate the numbers from Table 1 of \citet{BlakeBridle}
using
\begin{align}
z_0 & = 0.055 (r_{\rm lim}-24)+0.39 \nonumber
\\
\Sigma_0 &=  \frac{35400}{60^2} \left(\frac{r_{lim}}{24}\right)^{19},
\label{rlimdef}
\end{align}
where $r_{\rm lim}$ is the limiting apparent magnitude in the SDSS $r$ filter and $\Sigma_0$ is an overall surface density in units of deg$^{-2}$. We use (\ref{rlimdef}) to scale the number density of galaxies with area relative to the fiducial values.
We estimate the change in the limiting magnitude, $\Delta_{\rm mag}$, with area in the usual way as
\begin{equation}
\Delta_{\rm mag} = -1.25log_{10}\left(\frac{A}{A_{\rm fid}}\right) .
\end{equation}

As one might expect, the benefits of going to larger survey area are now less strong because of the lost depth, with the shear-only power law dependence weakening to $x$ = 0.3 for DE and 0.35 for MG.
The
baseline
result for cosmic shear analyses is that it is still better to go to larger survey area despite the lost depth.
Figure 4 of \citet{amarar07} suggests that, in the absence of a
CMB prior and IA modeling uncertainties, the FoM is roughly proportional to the square root of the area for fixed observing time. We find that  with the inclusion of CMB priors,
we obtain a similar result although the dependence on area is slightly weakened as in the constant depth case. When the galaxy position information is added the same story holds, but the dependence on area is slightly stronger.

When intrinsic alignments are added the improvement with increased area is significantly reduced, particularly in the case of $\epsilon\epsilon$-only which reduces to a power law of $x\sim$0.15 for DE.
A survey with an area of $5,000$deg$^2$ gathers $\sim 80\%$ of the DE information of a $\sim 20,000$deg$^2$ survey. This is because intrinsic alignments dominate over cosmic shear at very low redshift, and by marginalising out the intrinsic alignment nuisance parameters we effectively remove the information at low redshift. Therefore we expect any cosmological figure of merit to eventually reduce in the (unphysical) limit of very large area and small depth.

The galaxy clustering alone constraints are insensitive to the intrinsic alignments and therefore increase as usual.
When using the full $\epsilon\epsilon+n\epsilon+nn$ dataset there is more benefit to increased area even with the tradeoff of shallower depth, although in this case a 5000 square degree survey still gathers $\sim 70\%$ of the information
in a $\sim 20,000$deg$^2$ survey. Note that the exact details of these conclusions will depend on the fiducial intrinsic alignment model and the number of intrinsic alignment and bias nuisance parameters marginalised over.
Without galaxy position-shear cross correlations the constraints bear out the same trend in relative FoM with area, just with an overall lower magnitude.

 We repeated the exercise using the default survey parameters for a stage III survey in Fig.~\ref{fig:Aconstt_DES}.
 We vary the survey area for fixed survey time, and cover the same range as in Figure \ref{fig:varyA}.
 The survey depth for each survey time is correspondingly shallower, so that we have 10 galaxies per square arcminute for the 5000 square degree survey area.
The left-hand plots in Fig.~\ref{fig:Aconstt_DES} tell a similar story to those in Fig.~\ref{fig:varyA}. When a constant survey time is considered there is a more pronounced tailing off of improvement with area above $\sim 5-10,000$ deg$^2$ than was the case for a stage IV survey. As the area is increased above 5000deg$^2$ the number density of galaxies falls from its fiducial value of 10 arcmin$^{-2}$. For larger areas the number density becomes very low and shot noise rapidly increases at small scales. The loss of information from non-linear scales has a strong impact on the constraining power of the survey, limiting the improvement with increasing area. The fact that $nn$ alone does not display this behaviour, displaying the same behaviour for fixed depth or survey time, supports our analysis- the galaxy position information is already subject to a stringent cut on $l$, removing the nonlinear information and protecting nn from the impact of shot noise.

\begin{figure*}
  \begin{flushleft}
    \centering
       \includegraphics[width=4in,height=4.35in]{deltaz_Nz_fig_wCMB.ps}
\caption{Figures showing the variation with photometric redshift error ($\delta_z$) [left panels] and number of tomographic bins ($N_z$) [right panels] of a Stage IV survey FoM for dark energy (DE) parameters $w_0,w_a$ [blue lines], and modified gravity (MG) parameters $Q_0, Q_0(1+R_0)/2$ [red lines], as described in (\ref{DEFoM}) and (\ref{MGFoM}), relative to a `baseline' FoM [black cross].
 All FoMs contain priors from a Planck-like CMB survey. The figures show  relative FoMs for an optimistic scenario, in which uncertainities in the IA model have been excluded, [top panels] and a conservative scenario, in which uncertainties in the IA model are marginalized over using a $N_k=N_z=5$ gridded bias model [lower panels].  Three data combinations are considered in each panel, shear-shear correlations `$\epsilon\epsilon$' [dotted lines], '$\epsilon\epsilon$+$nn$' [dashed] and when shear-position cross-correlations `$n\epsilon$' are included [full lines]. For the galaxy position correlations a $N_k=N_z=5$ gridded bias model is used throughout.
 }
\label{fig:FoM_Deltaz_Nz}
  \end{flushleft}
\end{figure*}

\subsection{Photometric Redshifts}
\label{sec:photo-zs}

Accurate redshift information is essential for weak lensing tomography. As survey size increases
the number of redshifts becomes of order hundreds of millions to billions.
The expense of acquiring spectroscopic redshifts for this number of galaxies is prohibitive. As a result the next generations of WL surveys will rely on photometric redshifts.

In this paper we follow the photometric redshift model of \citet{amarar07}, including the statistical dispersion of measured photometric redshifts and catastrophic failures in redshift measurement.
The statistical dispersion is modelled assuming that measured photometric redshifts, $z_{phot}$ are described by a Gaussian probability distribtion centred on the true redshift, $z_t$.
The width of the Gaussian is
the photometric redshift error which we assume scales as $(1+z)$ and is controlled
by a paramter $\delta_z$,
\begin{equation}
P_{z_{phot}}(z) = \frac{1}{\sqrt{(2 \pi) \delta_z (1+z)}}
\end{equation}

In addition to this uncertainty in the photometric PDF galaxy redshifts can be entirely misidentified if, for example, the wavelength range examined is insufficient to identify important features in the spectrum. A misidentified redshift can be said to be assigned a redshift offset by an amount $\Delta_z$ from the true redshift. Affected galaxies are known as catastrophic outliers.

We construct a PDF to describe these catastrophic outliers, $P_{z_{cat}}$, and combine it with the scatter PDF, writing the full probability distribution
\begin{equation}
P(z_{phot}|z_t) = (1-f_{cat})P_{z_{phot}}(z_t) + (f_{cat})P_{z_{cat}}(z_t)
\end{equation}
where $f_{cat}$ is the fraction of our measured galaxies which suffer catastrophic redshift estimation failures.

The distribution of $P_{z_{cat}}$ is bimodal, reflecting the fact that galaxies can either be misidentified as being higher or lower than their true redshifts,
\begin{equation}
<P_{z_{cat}^{\pm}}> = z_{cat}^{\pm} = z_t \pm \Delta_z
\end{equation}
where $P_{z_{cat}} = P_{z_{cat}^{-}} + P_{z_{cat}^{+}}$.
The value of $\Delta_z$ will depend on the survey filters, photometric reconstruction technique and spectral properites of the survey galaxies. We assume a fiducial value of $\Delta_z = 1$ and that the uncertainty on the PDF of the population of catastrophic outliers is the same as for the slightly scattered galaxies
\begin{equation}
\sigma(P_{z_{cat}^{\pm}}) = \delta_z(1+z_{cat}^{\pm}).
\end{equation}

Fig.~\ref{fig:FoM_Deltaz_Nz} shows the variation in FoMs with $\delta_z$ and $N_z$, the number of tomographic redshift bins, for different combinations of observables, with and without IAs.
There is a parallel between varying $\delta_z$ and $N_z$, lower redshift error focuses the redshift distribution per bin into a tighter shape, allowing us to learn from changes with redshift over shorter intervals. Somewhat equivalently, increasing the number of tomographic bins gives us, in principle, the opportunity to detect changes with redshift at higher resolution.

Increasing $\delta_z$ decreases the constraining power of a survey because the loss of redshift precision ``smears out'' the redshift distribution of our sample. In particular galaxies leak between tomographic redshift bins meaning we can extract less information from tomography.
         The impact of increased $\delta_z$ on constraining DE is relatively weak. For $\epsilon\epsilon$ alone the
 DE FoM drops by only 1/4 from $\delta_z = 0$ (spectroscopic, i.e. perfect redshift precision) to $\delta_z = 0.2$, already a much higher value than the fiducial redshift uncertainties of
typical Stage-III ($\delta_z = 0.07$) and Stage-IV ($\delta_z = 0.05$) experiments. MG FoMs display very similar behaviour.

The reasons for this behaviour are relatively well understood. The lensing integral (eqn.~\ref{eqn:GG_integral}) modulates the 3D matter power spectrum by the lensing weight functions $W_{i}(\chi)$ of the redshift bins being correlated. These lensing weight functions are broad in redshift and act as kernels
smoothing the redshift information. This effect limits the usefulness of increasingly accurate redshift information and explains why the trend with $\delta_z$ is relatively shallow.

The inclusion of galaxy position information produces slightly steeper trends with increased $\delta_z$ because their angular power spectra integrals are modulated by the redshift distribution $n_{i}(\chi)$ of the bin(s) in question. This is a narrower function than the lensing weight function, providing more scope for improvement in accuracy with better redshift information.

The slightly steeper curves for the MG FoMs may come from the larger number of parameters that need to be constrained in this model, which can be done with new types of information such as increased redshift resolution. Broadly the results for modified gravity are very similar to those for dark energy.

All of the
data combinations with varying number of tomographic redshift bins, $N_z$, show the characteristic plateaux behaviour
as $N_z$ increases.
Without IAs, increasing the number of tomographic bins becomes much less effective after $N_z \sim2-3$. After this number $\epsilon\epsilon$ is effectively flat, while $\epsilon\epsilon + nn$ and $\epsilon\epsilon + n\epsilon + nn$ flatten out slowly, gaining little benefit after $N_z \sim 10-12$. This behaviour is expected as the lensing kernel and photo-z scatter smear-out redshift information and limit the ability of ever-finer tomography to improvement our knowledge of redshift behaviour.

The inclusion of IA terms for the same probes
shows the expected decrease in constraining power as we have to marginalise over IA bias terms. As we have seen previously, the MG FoMs take more of a hit due to the IA effects than their DE counterparts.

The decrease in FoM with increasing $\delta_z$ is significantly more pronounced once IAs are included, with $\epsilon\epsilon$ including IAs losing ~$67\%$ of its constraining power for DE  over the interval probed. Qualitatively we might hope to understand this from the fact that the cosmic shear and each IA term, II and GI, have a different redshift dependence. Improved redshift knowledge allows us to better discriminate between the cosmic shear signal and IA contamination. This produces stronger constraints on the IA nuisance parameters and consequently tighter constraints on DE and MG.

Similarly, the presence of IAs for increasing $N_z$ makes the plateau behaviour less pronounced and pushes the levelling off of the FoM back towards $N_z\sim 6-10$ for all probes. The use of smaller slices in redshift space gains information about the z-distribution of the IA signals and aids our constraining power. The effect is curtailed by the same fundamental limit of redshift accuracy in the projected angular power spectrum. Again the hit on FoM due to IAs is more pronounced for MG than DE but the qualitative behaviour of FoM with $N_z$ in the presence of IAs is the same for both.

The effect of changing the fraction of catastrophic outlier redshifts in our survey sample is shown in Fig.~\ref{fig:FoM_fcat}. As we would expect increasing the fraction of completely mis-estimated redshifts makes for poorer constraints on both MG and DE, for similar reasons to the impact of $\delta_z$.

All the lines in Fig.~\ref{fig:FoM_fcat} are relatively flat, suggesting that the projected nature of the angular power spectra and the effect of the lensing weight function continue to limit the impact of redshift information on overall constraints. No probe combination loses more than 30\% of its DE or MG FoM as $f_{cat}$ increases from zero to 0.2 which is more than twice the typical requirement value of projected stage-IV surveys.

The general pattern that MG FoMs decrease slightly more strongly than their DE equivalents with the addition of IAs is repeated here. One interesting feature is that the MG constraints for $\epsilon\epsilon$ without IAs and all the probe combinations with IAs decrease more steeply with increasing $f_{cat}$ than their DE counterparts. The difference is most pronounced for $\epsilon\epsilon$ with IAs where the MG constraints falls by twice as much over the $f_{cat}$ range as the DE FoM.

\begin{figure}
  \begin{flushleft}
    \centering
       \includegraphics[width=3.5in,height=2in]{fcat_fig_wCMB.ps}
\caption{Figures showing the variation with fraction of catastrophic redshift outliers ($f_{cat}$) of a Stage IV survey FoM for dark energy (DE) parameters $w_0,w_a$ [blue lines], and modified gravity (MG) parameters $Q_0, Q_0(1+R_0)/2$ [red lines], as described in (\ref{DEFoM}) and (\ref{MGFoM}), relative to a `baseline' FoM [black cross].
The figures show  relative FoMs for an optimistic scenario, in which uncertainities in the IA model have been excluded, [left panel] and a conservative scenario, in which uncertainties in the IA model are marginalized over using a $N_k=N_z=5$ gridded bias model [right panel].
Notation is the same as in Fig.~\ref{fig:FoM_Deltaz_Nz}}
\label{fig:FoM_fcat}
  \end{flushleft}
\end{figure}

\subsection{Redshift Priors}
\label{sec:zpriors}

As well as describing the change in FoMs with the size of the photometric uncertainty, it is important to the quantify the effect of our \emph{degree of knowledge} of that uncertainty on our FoMs. In our case we follow the standard procedure of inserting this degree of knowledge as a prior on the parameter $\delta_z$.

To test this assumption we extend our photometric redshift model- breaking the redshift range 0-3 into 30 bins in z of width 0.1,
following \citet{mahh06}. Note that these bins are used only to parameterise our photo-z model, we continue to use the standard 10 bins for shear tomography. We allow $\delta_z$ to vary independently in each of these z-bins and introduce a new parameter $z_{\textrm{bias}}$, the amount by which the the photometric distribution of redshifts is offset from the true redshift. $z_{\textrm{bias}}$ is allowed to vary independently in each of our 30 z-bins. Each $\delta_z$ has the standard fiducial value of 0.05 and each $z_{\textrm{bias}}$ has fiducial value zero. We have enlarged our Fisher Matrix by 60 new free parameters, all of which are marginalised over in the results given in Fig.~\ref{fig:zbias_beta}.

We assume that the Gaussian prior assigned to each of the 60 free parameters is the same and we allow the prior to vary from a width of 0.0001 to 10. Clearly the constraining power a given survey achieves decreases as we reduce our amount of prior knowledge about the redshift distribution.

Apart from the expected decrease with wider priors we see two distinct regimes- relatively flat FoMs below prior ~ 0.001 and above 1 with a transition regime in between.
It is reassuring to note that we cannot achieve arbitrarily large FoMs with tighter redshift priors- this is connected to the previously noted ``smearing'' of redshift information in the shear angular power spectrum.
At large values of the prior width we reach a ``self-calibration'' regime in which the information in the survey is sufficient to constrain the unknown parameters \citep{Huterer:2005ez}.

When IAs are ignored, the decrease in constraining power is more pronounced. Mostly the lines start higher for narrow priors and converge to a similar low FoM for wide priors. It is probable that this behaviour is an artefact of there being fewer nuisance paramters when IAs are ignored. This produces strong constraints in the narrow prior regime, while for wide priors redshift uncertainty becomes the dominant source of error and probe combinations with/without IAs are more similar in terms of constraining power.

What is also clear is that the DE FoM falls off much more steeply over the prior range than the MG FoM. This is true for all probe combinations, with and without IAs but is most spectacular for the lines which include IAs, here there is actually a cross-over, with the MG FoM ending higher than the DE FoM at prior = 10. For $\epsilon\epsilon$, including IAs, we observe a a factor of 10 decrease in DE FoM but a factor of 3 decrease in MG FoM. This trend is repeated when all data is used ($\epsilon\epsilon+n\epsilon+nn$). We expect the dark energy parameter $w_a$ to be particularly sensitive to incomplete redshift information because it describes the time evolution of the dark energy equation of state. By contrast, the modified gravity parameters are more robust to poor redshift knowledge because they are constant with redshift and the redshift evolution of the modified gravity functions is fixed by our choice of redshift power law $s$.

We can relate the required prior on redshift parameters to the number of spectroscopic quality galaxy redshifts needed to calibrate our photometric sample as given by \citet{mahh06}:
\begin{equation}
N_{spec} = \frac{2\delta^{2}_{z}(1+z)^2}{\Delta^{2}_{\sigma_{z}}}.
\end{equation}

For $\epsilon\epsilon$, ignoring IAs, a prior of 1$\times 10^{-3}$ is sufficient to recover 90\% of the peak FoM for either DE or MG. With a photometric scatter of $\sigma_z = \delta_{z}(1+z) = 0.05(1+z)$ and a median redshift of $z_m = 0.9/\sqrt{2}$, this translates into 1.34$\times 10^4$ spectroscopic galaxies per redshift bin. Including all galaxy position information $\epsilon\epsilon + n\epsilon + nn$ and IAs reduces the required prior to $\sim 1\times 10^{-2}$, or
of order 100 spectroscopic galaxies per bin. This represents a factor of 100 reduction in required numbers compared to the standard result. However, note that there are other factors which must be taken into consideration when determining the number of galaxies per redshift bin, especially cosmic variance \citep[e.g. see the discussion in][]{VanWaerbeke:2006qt,ishakh05,bordoloiea08,cunha11}.

Fig.~\ref{fig:zbias_beta_DES} shows similar results for the fiducial stage III survey. Qualitatively the results are similar to the Euclid survey. In this case, with a photometric scatter of $\sigma_z = 0.07(1+z)$ and a median redshift of $z_m = 0.8/\sqrt{2}$, $\epsilon\epsilon$ without IAs requires a prior of $\sim 1\times 10^{-2}$ which translates into around 100 galaxies per bin. Again $\epsilon\epsilon + n\epsilon + nn$ with IAs requires a factor of 100 fewer galaxies, a substantial saving. Interestingly, while the MG constraints produce the same saving in spectroscopic galaxies required when IAs and all probes are included, the absolute values are a factor of 2 tighter in prior terms (i.e. a factor of 4 more spectroscopic galaxies are required).

\begin{figure}
  \begin{flushleft}
    \centering
       \includegraphics[width=3.5in,height=4in]{zbias_fig_wCMB.ps}
\caption{Figure showing the variation with the priors on the bias on median redshift, $z_{bias}$, of 30 redshift bins spanning the survey redshift range due to the effect of photometric redshift uncertainties and the photometric redshift statistical dispersion, $\delta_z$. All other survey parameters are fixed at their fiducial values. Results are for a Stage IV survey FoM for dark energy (DE) parameters $w_0,w_a$ [blue lines], and modified gravity (MG) parameters $Q_0, Q_0(1+R_0)/2$ [red lines], as described in (\ref{DEFoM}) and (\ref{MGFoM}), relative to a `baseline' FoM [black cross].
As shown in the key in the figure, shear-shear correlations, `$\epsilon\epsilon$' , and combined shear and galaxy position information including cross-correlations '$\epsilon\epsilon$+$n\epsilon$+$nn$' are shown
when uncertainities in the IA model have been included and excluded.}
\label{fig:zbias_beta}
  \end{flushleft}
\end{figure}

\begin{figure}
  \begin{flushleft}
    \centering
       \includegraphics[width=3.5in,height=4in]{zbias_fig_DES_wCMB.ps}
\caption{As in figure \ref{fig:zbias_beta}, but for a Stage III survey specification.
}
\label{fig:zbias_beta_DES}
  \end{flushleft}
\end{figure}

\section{Conclusions}
\label{sec:conclusions}

Previous work \citep{joachimi_bridle_2009} has shown how constraints from cosmic shear are significantly degraded by the inclusion of IA systematics, using a comprehensive grid of k- and z-dependent nuisance parameters. The impact of IAs can be mitigated by the inclusion of galaxy position correlations and shear-position cross-correlations using only the photometric information gathered as part of the shear survey.

In a related paper, LBKB, we confirmed and extended these results showing that using the full $\epsilon\epsilon+n\epsilon+nn$ information with up to
36 nuisance parameters can regain the constraining power of a naive $\epsilon\epsilon$ analysis which ignores IAs and that this is true when constraining deviations from GR as well as dark energy. In this paper we confirm that these results are robust across a wide range of survey parameters, encompassing fiducial stage III and stage IV cosmic shear surveys.

Increasing survey area improves both DE and MG FoMs, as one expects, whether we ignore or include intrinsic alignments. Previous work, which did not include the CMB, found a linear variation of FoM with survey area even when IAs were included.  We have shown, however, that if one includes priors from upcoming CMB experiments that  this dependence is weakened to vary roughly as a power law with an exponent of 0.4. The inclusion of IAs slightly weakens the relationship between FoM and survey area.

More realistically, survey time is a constant so increased area must be traded off against depth.
We find the dependence on area significantly flatter when IAs are included. This is to be expected because IAs dominate over the cosmic shear signal at low redshift and we effectively marginalise out information from this regime. On increasing the area, and thus decreasing the depth to lower redshift, an increasing proportion of the signal is lost by the marginalisation.
The effect is more pronounced for a stage-III survey than a stage-IV survey, which is expected because the depth is very shallow indeed if it has to cover half of the sky.

The quality of photometric redshift information is crucial to the success of future cosmic shear missions. We show that the inclusion of IAs and the full use of galaxy position information requires more accurate redshift information than a naive cosmic shear only analysis. GG-only retains 97\% of its constraining power for perfect redshift knowledge with a photometric redshift scatter of $\delta_z = 0.05$, the fiducial value for a stage-IV survey. By contrast the combined $\epsilon\epsilon + n\epsilon + nn$ including IAs requires $\delta_z = 0.015$ to retain the same proportion. Similarly the characteristic plateau behaviour, where WGL constraining power does not improve over a tomographic resolution of $\sim 4-5$ bins is pushed back on the inclusion of IAs and galaxy position information to $\sim 10-12$ redshift bins. This is understandable as the standard cosmic shear redshift dependence, modulated by a broad lensing kernel for each redshift bin, has been joined by galaxy position correlations which depend on the narrower redshift distribution function and the attempt to constrain IAs signals, each with their own redshift dependence. Taken together these place increased importance on accurate redshift knowledge.

We have presented results showing the effect of varying our degree of prior knowledge of an extended parameterisation of redshift information, allowing 60 free parameters to be shared between redshift dispersion and redshift bias.
Around $\Delta z_{\textrm{bias}} = \Delta \delta_z = 0.1$ the probes enter a self-calibration regime and the FoMs flatten off. The DE FoMs fall off much more steeply and relatively lower than their MG counterparts, highlighting the sensitivity of the DE equation of state, particularly $w_a$, to incomplete redshift information. By contrast the MG parameters have a fixed redshift dependence in this work.

This work assumed a relatively simple model for deviations from GR. We vary two MG parameters, each with the same fixed redshift dependence. In the future we hope to extend these results to take into account a MG gravity parameterisation which could vary more generally as a function of scale and redshift. In contrast our fiducial parameterisation of galaxy bias and IA nuisance parameters is robust, containing over 100 nuisance parameters. With a greater understanding of the physics of galaxy formation mechanism responsible for IAs it may be possible to motivate a more targeted, simpler parameterisation.

\section*{Acknowledgements}

We thank the Aspen Center for Physics for support and for hosting two coincident workshops on ``Wide-Fast-Deep Surveys: New Astrophysics Frontier" and  ``Testing General Relativity in the Cosmos" in 2009 where this work was conceived. The authors would like to thank the Kavli Royal Society International Centre for hosting the ``Testing general relativity with cosmology" workshop in 2011 that supported fruitful discussion and collaboration.

We thank Filipe Abdalla, Adam Amara, David Bacon, Scott Dodelson, Ole Host,  Martin Kilbinger, Andrew Jaffe,  Bhuvnesh Jain, Benjamin Joachimi, Ofer Lahav, Rachel Mandelbaum, Anais Rassat, Shaun Thomas, Alexandre Refregier and Jochen Weller for helpful discussions.

We are very grateful to Josh Frieman for spotting an error in equation 19 of the first version, and to Barney Rowe for discussing with us the correction.

RB's and IL's research is supported by NSF CAREER grant AST0844825, NSF grant PHY0968820, NASA Astrophysics Theory Program grants NNX08AH27G and NNX11AI95G and by Research Corporation.
SLB thanks the Royal Society for support in the form of a University Research Fellowship
and acknowledges support from European Research Council in the form of a Starting Grant with number 240672.
\bsp

\bibliographystyle{mn2e}

redpajama_set_name: RedPajamaArXiv


text:
Why Losing is Good for Our Kids
Sam Weinman
By Sam Weinman
The first thing you should know about helping your kids to learn to lose is that it's still going to be difficult for them. They wouldn't be human otherwise. If we're talking about loss, or setbacks, or whatever the word is you want to use, the universal ingredient is disappointment, and everyone is entitled to their momentary period of wallowing.
I spent two years researching and writing a book on the topic of losing, and my two sons are still prone to slammed hockey sticks and tears in the locker room. The difference now is they have a better understanding that most losses are fleeting. Even better, somewhere in the back of their 11- and nine-year old brains is the abstract notion that the experience may even be good for them.
In consulting with psychologists and coaches, as well as a selection of athletes and celebrities who endured notable losses, this last part proved to be the foundation of my book — that losing has very real, very tangible benefits, both in how it shapes our character and in the way it helps us identify areas of weakness that need addressing. As a parent, I suspect this all makes sense to you. If you make three fielding errors in a baseball game, that's a signal you need infield practice. If you didn't get the big account at work because your presentation was a mess, that's a hint you probably need to be better prepared.
Surely all of us can point to setbacks in our lives that proved to be instructive. But at least in the immediate outset, kids have a harder time seeing the upside. Allow me to offer some advice on how to help your kid deal with a loss.
Don't minimize the pain.
Much as we are blessed with the broader worldview that the fate of mankind does not hinge on a flag football game, your child has a hard time seeing that right now. Before you get anywhere else, you need to meet them where they are. And right now, all they know is it sucks. However trivial the setback might seem, your first move is to hear them out.
Spin the story forward.
After a recommended cooling-off period in which you've tended to any immediate wounds, your next objective is to shift focus from what was to what will be. With kids, there is always a next game or a next practice, and even if there isn't, there is always a next season. The most difficult part of losing is in harping on an outcome you can no longer control. The more you can shift attention toward something still in front of you, the quicker you can move on to it.
Stress honesty.
Even if the objective is to move forward, there's still an opportunity for your child to reflect on areas where they could have done better. This is a complicated concept for young kids, and any hope of eliciting constructive answers likely depends on your tone. With my kids at least, a gentle line of questioning is a lot more effective than a series of pointed criticisms. In other words, it needs to come from them. If your child lost a tennis match because he can't hit a backhand, ask him where he thinks he needs the most practice instead of telling him that his backhand is weak.
Redefine winning and losing.
The most effective counter to losing might actually be pre-emptive. Before your child even embarks on a game, stress to them goals that have nothing to do with winning and have more to do with a "process." Again, think about the things they can control. They can't guarantee they'll score a goal, but they can vow to be in the right position and to always hustle. Studies have shown that kids who are praised for effort as opposed to results or innate talents tend to be more adaptive and more willing to embrace challenge. It also allows them to feel successful even when the scoreboard says otherwise. By the way, this is not a concept reserved for kids. It's also an effective approach at the highest level of sports. "The people who are really successful, they're not thinking about winning and losing," says leading sports psychiatrist Dr. Michael Lardon, who works with a number of professional athletes. "They're thinking about the execution of what they can do."
Celebrate resilience.
Far more impressive than when your kid wins a big game is when he or she handles disappointment well. Don't let these moments go unacknowledged. In the same way it's important to praise effort more than results, you want to make your child aware of their ability to weather difficult situations effectively. This is a gift that keeps on giving. If your kids know they can handle a tough outcome once, they know they can do it again.
character traits, Kids loosing, montessori mom, parent
Sam is the author of Win At Losing: How Our Biggest Setbacks Can Lead To Our Greatest Gains. He is also the Digital Editor of GolfDigest.com, where, during his tenure, the website has grown to boast the largest digital audience in golf and has been nominated for several National Magazine Awards. Prior to that, he was the lead hockey and golf writer for The Journal News and LoHud.com, during which time he launched the popular hockey blog, "Rangers Report." For more than a decade, Sam's work has regularly appeared in Gannett newspapers around the country, including USA Today, and he has contributed to Golf World, Yahoo! Sports, ESPN the Magazine, and Sports Illustrated. He is the recipient of several national writing awards for his coverage of the PGA Tour and the National Hockey League. He lives with his wife and two boys in Rye, NY, where he coaches multiple youth sports teams.

redpajama_set_name: RedPajamaCommonCrawl


This small landing is at the west end of the Bronx Kill near its confluence with the Harlem River. There is a large combined sewer overflow outfall on the Bronx side of the channel a few yards to the east. Site Coordinator: Chris Girgenti. Site Sponsor: Randalls Island Park Alliance. These samples are processed by Columbia University/Lamont-Doherty Earth Observatory (Wade McGillis).


text:
"All New" New Mexico Lobo Offense
Wayne Dzubak
New Mexico football is averaging more passing yards than rushing yards in well it seems for ever!
Once a triple option offense the Lobo's have switched to more of a spread option system. That's right, the word option is still part of the offensive verbiage but it just means they're more likely to pass the football than ever before.
New Mexico head coach Bob Davie brought in a new offensive coordinator named Calvin Magee and he's made some significant changes but there is still an option component the Broncos have to be prepared to face.
Boise State STUD end Curtis Weaver told media members the New Mexico offense is more like a regular offense than the triple option look of the past.
The look has given BSU trouble and although the Broncos are 8-1 all time against the Lobos, there have been some close calls because of the physical nature of the triple option.
The Lobo's currently average 163 yards rushing and 198 yards passing a game as they adapt to their new look.
Make no mistake, this is a dangerous game for Boise State. It's a road-tweener game. By tweener I mean it's a must win game on the road sandwiched between two home games versus ranked opponents. Boise State took care of 16th ranked Fresno state but they can't afford to look forward the no.14 Utah State a week from Saturday.
That's why every player I've interviewed this week won't even consider talking about the overall picture, they can't see past Friday night.


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text:
Q: unable to find valid certification path to requested target in java while authenticating Multiple URLs I'm trying to perform ssl certificate authentication for Multiple URLs in a java program, I have stored URLs in a arrayList and pass them as a parameter to the method which performs certificate authentication and provides the response from the server.
The above method is working fine for single URL in case of  both manual entry and through the variable. But By passing these URLs through an arrayList is getting success for the first iteration from the second URL till the nth URL I'm getting this error.
javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
    at sun.security.ssl.Alerts.getSSLException(Alerts.java:192)
    at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1964)
    at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:328)
    at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:322)
    at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1614)
    at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:216)
    at sun.security.ssl.Handshaker.processLoop(Handshaker.java:1052)
    at sun.security.ssl.Handshaker.process_record(Handshaker.java:987)
    at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1072)
    at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1385)
    at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1413)
    at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1397)
    at sun.net.www.protocol.https.HttpsClient.afterConnect(HttpsClient.java:559)
    at sun.net.www.protocol.https.AbstractDelegateHttpsURLConnection.connect(AbstractDelegateHttpsURLConnection.java:185)
    at sun.net.www.protocol.http.HttpURLConnection.getInputStream0(HttpURLConnection.java:1564)
    at sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1492)
    at java.net.HttpURLConnection.getResponseCode(HttpURLConnection.java:480)
    at sun.net.www.protocol.https.HttpsURLConnectionImpl.getResponseCode(HttpsURLConnectionImpl.java:347)
    at com.redeem.GetREsponse.getResponse(GetREsponse.java:108)
    at com.redeem.Check1.main(Check1.java:20)
Caused by: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
    at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:397)
    at sun.security.validator.PKIXValidator.engineValidate(PKIXValidator.java:302)
    at sun.security.validator.Validator.validate(Validator.java:262)
    at sun.security.ssl.X509TrustManagerImpl.validate(X509TrustManagerImpl.java:324)
    at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:229)
    at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:124)
    at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1596)
    ... 15 more
Caused by: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
    at sun.security.provider.certpath.SunCertPathBuilder.build(SunCertPathBuilder.java:141)
    at sun.security.provider.certpath.SunCertPathBuilder.engineBuild(SunCertPathBuilder.java:126)
    at java.security.cert.CertPathBuilder.build(CertPathBuilder.java:280)
    at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:392)
    ... 21 more
javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: **unable to find valid certification path to requested target**

How to resolve this issue?
Code snippet where I have followed different methods
public class Check1 {
static ArrayList<String> arrayList;
public static void main(String[] args) {
    arrayList = new ArrayList();
    //GetREsponse get = new GetREsponse();
    arrayList.add("URL1");
    arrayList.add("URL2");
    arrayList.add("URL3");

    for(int i = 0; i < arrayList.size(); i++)
    {
        //GetREsponse get = new GetREsponse();
        GetREsponse.getResponse(arrayList.get(i));

    /*  Thread1 t = new Thread1(i);
        t.start(); // try to check the status by providing delay
        try {
            t.sleep(10000);
        } catch (InterruptedException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        //continue;
        */
    }
}

/*public static class Thread1 extends Thread
{
    int i1;
    public Thread1(int i)
    {
        i1 = i;
    }
    public void run()
    {
        //GetREsponse get = new GetREsponse();
        //get.getResponse(arrayList.get(i1));
        GetREsponse.getResponse(arrayList.get(i1));
    }
}

}*/

public class GetREsponse {
    static SSLContext sslcontext;
    static String Result;
    static StringBuilder sb = new StringBuilder();

    static void getResponse(String url)
    {
        System.out.println(url+"~swa");
        try {

            sslcontext = SSLContexts.custom()
                    .loadTrustMaterial(new File("C:/ssl/1.jks"),
                            "secret".toCharArray())
                    .loadKeyMaterial(new File("C:/ssl/certificate.p12"),
                            "secret".toCharArray(),
                            "secret".toCharArray())
                    .build();

            System.out.println("success");
        } catch (KeyManagementException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (UnrecoverableKeyException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (NoSuchAlgorithmException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (KeyStoreException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (CertificateException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        // Allow TLSv1 protocol only
        SSLConnectionSocketFactory sslsf = new SSLConnectionSocketFactory(
                sslcontext,
                new String[] { "TLSv1" },
                null,
                SSLConnectionSocketFactory.getDefaultHostnameVerifier());

        // Directly configure HTTP authentication
        System.setProperty("javax.net.ssl.keyStore", "C:/ssl/1.jks");
      System.setProperty("https.protocols", "TLSv1");
      System.setProperty("javax.net.ssl.keyStorePassword", "password");
        String username = "Username";
        String password = "loginpassword";
        String authString = username + ":" + password;
        String authHeader= null;
        try {
            authHeader = "Basic " + Base64.getEncoder().encodeToString(authString.getBytes("UTF-8"));
            System.out.println("success");
        } catch (UnsupportedEncodingException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        CloseableHttpClient httpclient = HttpClients.custom()
                .setSSLSocketFactory(sslsf)`enter code here`
                .build();
        System.out.println("success");

        try {

            URL url1 = new URL(url);

            HttpsURLConnection urlConn = (HttpsURLConnection) url1.openConnection();
            urlConn.setDefaultSSLSocketFactory(sslcontext.getSocketFactory());
            System.out.println("1");
            urlConn.setDoOutput(true);
            urlConn.setRequestMethod("GET");

            urlConn.setRequestProperty("Authorization", authHeader);
            urlConn.setRequestProperty("Content-Type", "application/json");
            urlConn.setRequestProperty("Accept", "application/json");

            System.out.println("2");

           int response = urlConn.getResponseCode();
            System.out.println("3");

            System.out.println(response+"");

            InputStream inputStream = urlConn.getInputStream();
            InputStreamReader i_reader = new InputStreamReader(inputStream);
            BufferedReader b_reader = new BufferedReader(i_reader);
            String str = b_reader.readLine();
            while(str != null)
            {
                sb.append(str);
                str = b_reader.readLine();
            }
            Result = sb.toString();
            System.out.println(sb.toString());
            }
            catch(Exception e)
            {
                e.printStackTrace();
                System.out.println(e.toString());
            } finally {
            try {
                httpclient.close();

            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
                System.out.println(e.toString());
            }
        }
    }
}

redpajama_set_name: RedPajamaStackExchange


Since 2014, The Divergent Series has been trying to capitalize on the young adult action movie craze and achieve the same kind of success as The Hunger Games. Unfortunately for the franchise, it's achieved nothing of the sort, drawing mixed to negative reception and not earning a lot of money for movies that big. The fourth and final entry, The Divergent Series: Ascendant, was originally supposed to be released in theaters on June 9, 2017, but now news just hit that the story will instead try to continue on the small screen.
The latest from Variety states that Ascendant is now being planned as a television movie, and following that, the story will continue in a standalone TV series. Lionsgate will still be handling production, and the goal is to conclude the stories of the current Divergent cast in the TV movie to make way for a new group of characters on the show. Lee Toland Krieger is still set to direct the new version of Ascendant, but there's no word yet on if Shailene Woodley, Theo James or any of the other movie stars will reprise their characters for the final movie. There's also no word on which network or stream service will air both projects, as no deal has been reached yet.
Needless to say, this is huge demotion for The Divergent Series, but given how these movies have been performing, this decision is understandable. The Divergent Series: Allegiant was the worst of the bunch, earning almost universally negative reviews and only earning a little over $179 million worldwide. This performance led Lionsgate to reevaluate their strategy for the series, and rather than just never release the final movie, they've opted to bring it to TV and try to keep this universe going in a series. Obviously Ascendant's budget will be reduced, but compared to the movie going up against World War Z 2 and The Mummy reboot in its original release date, turning to TV was a safer bet for any kind of success.
This is the latest, but definitely most impactful, obstacle The Divergent Series: Ascendant has recently faced. First, Robert Schwentke, director of the second and third movies, departed, leaving Lee Toland Krieger to take his place. Then Ascendant's budget was reportedly reduced. While this isn't the first time a movie story has continued on television (Stargate comes to mind), bringing the Divergent universe to television is a drastic move. Whether the series will fare better on TV remains to be seen, but at least now, it won't have to worry about being brutalized on the big screen.
What do you think of this news? Are you glad The Divergent Series may continue on television or do you wish it had just ended immediately? Let us know your thoughts in the comments below.


Kent Gration's design/art philosophy highlights the need for renewable resources through an approach to process that carries the sentiment of 'good design should last forever, but needn't last forever in the environment'. The questioning of consumer needs has allowed Kent to develop works that factor in the whole-of-life cycle of a product, from its material origin and benefits, biodegradability and recyclability while exploring a conceptual approach to form and object. He addresses growing environmental concerns regarding mass-production and the overuse of non-renewable polluting materials in the furniture industry.
The Wambamboo by Kent Gration furniture range encompasses stylish and functional bamboo furniture and everyday objects. The range highlights bamboo's versatility as a natural high-end material using bamboo poles, cross-laminated boards and veneers, whilst promoting the environmental benefits of this rapidly renewable resource.


text:
Q: Running Bluestacks on AWS or Google cloud Is there any way to run Bluestacks on virtualized instances in the cloud.
I tried both AWS, GCE, both failed.
AWS fails with the message "Failed due to enabled Hyper-V", even It's disabled in the settings.
And GCE just cannot start bluestacks, it simply loads and finally says "couldn't start Bluestacks"
What's interesting that these guys managed to do it.. https://aws.amazon.com/marketplace/pp/Cognosys-Inc-1-Click-Ready-Secured-BlueStacks-App-/B01N0E0YYZ
But how? Cannot use their image as it's old.

A: Bluestack doesn't work for me too. I saw Cognosys but they are running on Windows Server 2012 which is really old.
Try Memu Play 2GB Ram Minimum (Try the 4GB VPS)
Windows Server 2019 Base
DirectX
It works for me


text:
Pfc. Joe Steimel who now lives in North Port is pictured recovering from battlefield wounds at the close of the Second World War in Vincennes, Ind. his hometown. Photo provided
Pfc. Joe Steimel of Holiday Park North Port received 2 Purple Hearts fighting Germans in WW II
Pfc. Joe Steimel of Holiday Park in North Port was a twice-wounded mortar-man who served in the 29th Infantry Division that fought its way through France and Germany during the last year of the war in Europe in World War II.
After basic in Texas, Steimel sailed for Europe aboard the cruise-ship "Aquitania" in July 1944. He reached Glasgow, Scotland in seven days. The new recruits took a train south to South Hampton, England. Two days later they boarded another ship that docked at Omaha Beach in France.
"They put us in trucks and took us to St. Lo, France. That's where I joined the 29th Division right after we got to St. Lo," Steimel said.
They made me an ammo bearer for a mortar unit," he explained almost 75 years later. "I was given a cape with six punches—three in the front and three in the back — that would carry six, .61-mm mortar shells. There was a gunner and two ammunition bearers in each mortar unit.
"Two days later we went into combat in France. We had to fall back because of the killed and wounded our division suffered," Steimel said. "I was the only one left in our mortar unit, so I became the gunner. The other two guys had been killed or wounded.
"When we returned to the front lines I was given two other people who became ammunition bearers.
"It was my job to set up the mortar and zero it in. I waited for my forward observer to give me coordinates where to fire. I would fire one round and then he would see where it hit and tell me. He would say go right or left, backward or forward.
"If I hit the target my observer would tell me to fire three rounds for effect. My ammunition man would drop three shells down the mortar tube after I made sure the gun was in line.
"Right after that we would take our mortar and run. In coming German artillery was headed our way. They had a couple of guys spotting our location along their front line.
"As the 29th got close to Paris, our division split off and went to western France. We were headed for a submarine base along the Atlantic coast that was as far north in France as you go along the coast," Steimel said.
"We went by a 40 & 8 train (40 soldiers and eight horses to a boxcar in World War I) to the sub base. We expected to capture the base from the Germans in nine days or less. It didn't work out that way. Two weeks later we were still trying to breech the thick concrete walls of the sub base.
"The Rangers were called in to do the job. They climbed up over the concrete walls and forced open the main front gates. We were waiting and charged in. Most of the Germans surrendered.
"The thing we had the most problem with were German snipers. We found out that some of the enemy snipers were French women who had German boyfriends.
"After we captured the sub base they put us on another 40 & 8 train and sent us back to eastern France. We ended up along the Siegfried Line separating Germany and France. We had no trouble going through the line because our infantry preceded us.
Steimel, 92, holds the Purple Heart he received when he was first wounded in France during WW II by enemy shrapnel. Sun photo by Don Moore
"When we got to Belgium my gun emplacement took a direct hit from enemy fire. It tore my gun up, but I pretty much escaped by jumping in a nearby slit trench. A sailor, who was working as an ammo bearer for me, jumped in right after I did and was hit in the back by shrapnel. I heard later that he died.
"For the next three days they gave me a rifle and I became an infantryman until they got another mortar. I was taking a little R & R in an old coal mine—taking a shower and cleaning up. My lieutenant asked me what was wrong with my left hip. I told him nothing, but he sent me to the aide station. They found a piece of shrapnel in my hip about the size of a pencil eraser. I got a tetanus show and was sent back to the front.
"On Oct. 27, 1944, my little brother's birthday, I received my first Purple Heart Medal. I sent it home.
"A short time later three of us mortar-men set up in a gravel pit south of Cologne, Germany. Our infantry was in front of us and our artillery was behind us, firing over our head. Someone put a short round in and our artillery hit a bunch of our people and killed them.
"I suffered a concussion from the incoming friendly fire. I was bleeding through my eyes and nose. They took me back to the aide station were I spent the next two or three days. Then they put me in a Piper Cub and I was flown to Paris. From there I was put in a C-47 (twin-engine transport) and flown to Oxford, England where I spent the next six weeks recovering."
They gave him an Oak Leaf Cluster to go with his Purple Heart. In other words, Steimel had been wounded twice on the battlefield.
"When I got out of the hospital in England they made a truck driver out of me," he said. "After Christmas '44 I was sent to La Havre, France. There I was used for whatever they needed me for. I became a driver for Lt. Col. Wilson in Germany.
"While there my old division, the 29th, came through going home. I asked them about going home with them, but they told me they had a complete division and couldn't take me.
"I told this to my colonel and he said, 'I can tell you how you can get home before them. Re-enlist for another 18 months.'
"I did and and because I already had accrued 90-points they put me on a liberty ship headed for Boston. It took a month to cross the Atlantic because of the rough water. Almost everyone on board was seasick.
"During the 90-day leave I got I got married to my wife, Judy. I was shipped back to a military field hospital in Antwerp, Belgium where I drove a medical supply truck all over Germany. Sixteen months later I returned to the U.S.A. and was discharged from the Army. It was 1946.
"I ended up working for General Motors at their Alison Aircraft Plant in Indianapolis, Ind. For the next 18 1/2 years I worked for them until I retired in 1990.
"Judy, my wife, and I bought an RV and toured the country for the next nine years. We hit all 50 states. The only one we didn't make in our RV was Alaska and Hawaii. In 1999 we sold our RV and retired to North Port.
The Steimels have six children, three boys and three girls: Jill, Sally, Sandy, Steve, Tim and Wayne.
D.O.B:
Entered Service:
Discharged:
Commendations:
Battles/Campaigns:
This story was first published in the Charlotte Sun newspaper, Port Charlotte, Fla. on Monday, August 21, 2017 and is republished with permission.
Click here to view the collections in alphabetical order in the Library of Congress. This veteran's story may not yet be posted on this site, it could take anywhere from three to six months for the Library of Congress to process. Keep checking.
Click here to view the War Tales fan page on FaceBook.
Click here to search Veterans Records and to obtain information on retrieving lost commendations.
All rights reserved. This copyrighted material may not be republished without permission. Links are encouraged.
08.23.2017 – U. S. Army / World War II / 29th Division / Aquitania


text:
Clearing house of the year: JSCC
Takehiro Hosomura, JSCC
Interest rate swaps
In the not-too-distant past there was a quite sizeable gulf in the cost of clearing interest rate swaps in the yen market's two clearing houses. Not any longer.
On August 8 last year, the basis on a 20-year yen swap between the Japan Securities Clearing Corporation and its rival LCH stood at 10.9 basis points. One year later, the gap is close to zero. That, dealers say, is a reflection of JSCC's success in attracting foreign firms through client clearing – a reason many see them as worthy winners of Asia's clearing house of the year.
"They are internationalising, and onboarding a lot of European firms through client clearing. As a result of that the basis between JSCC and LCH has shrunk," says a clearing and collateral manager for a global bank based in Tokyo. "It is all because of liquidity – most of the liquidity in the yen market is at JSCC, and so that is where people want to clear."
The yen rates market has traditionally been dominated by Japanese banks looking to hedge their loan books with fixed receiver swaps, which previously gave the CCP a highly directional book. That meant dealers could often only find counterparties willing to pay fixed at LCH, thereby duplicating their margin requirements, something that is reflected in swap prices at the two CCPs.
As European buy-side firms have begun to flock to JSCC in 2018, JSCC's book became less directional, leading to a dramatic narrowing in the basis. JSCC figures show the number of non-Japanese firms accessing the clearing house through client clearing now stands at 21, up from eight at the end of 2017.
A number of service improvements introduced at the CCP in the past year have helped to smooth the way for those foreign firms looking to access that deeper pool of liquidity for yen swaps.
In December 2018, for example, JSCC introduced an English language version of its interest rate swap clearing brokerage agreement, which is used by clearing brokers and their clients. Three months prior to that – in September – the CCP launched an in-house multilateral compression service for interest rate swaps. By stripping out the cost of third-party vendor fees, the CCP hopes the service will eventually lead to great compression.
Takehiro Hosomura, the executive officer in charge of clearing planning and OTC derivatives clearing services at JSCC in Tokyo, says such enhancements, along with greater swap liquidity, have helped to boost non-Japanese participation at the CCP. Comments made last year by Christopher Giancarlo, who was then the chairman of the Commodity Futures Trading Commission in the US, played an additional role in broadening such participation at the clearing house, he says. It was in London on September 4 last year that Giancarlo for the first time suggested that US clients would be able to clear swaps on non-US CCPs that aren't fully registered with the regulator, provided they have comparable supervision.
We continue to have dialogues with our members and continuously enhance our services, and we are seeing a lot of interest from offshore investors to join at JSCC. JSCC has the largest pool of liquidity for Japanese yen interest rate swaps, which also makes it attractive to clear here
"We continue to have dialogues with our members and continuously enhance our services, and we are seeing a lot of interest from offshore investors to join at JSCC," says Hosomura. "JSCC has the largest pool of liquidity for Japanese yen interest rate swaps, which also makes it attractive to clear here."
Helping CVA hedgers
The CCP is also working to help Japanese banks tackle one of the biggest challenges they have faced in recent years: the implementation of accounting credit valuation adjustments (CVA) for derivatives.
This is largely in response to increased demand from its Japanese megabank members for single-name credit default swaps due to their need to hedge the profit & loss (P&L) impact of CVA. Mizuho, SMBC Bank and Mitsubishi UFJ began to reflect the value of counterparty credit risk in their derivatives portfolios in 2018, and have established desks to hedge the volatility that the adjustment creates on their P&L.
The annual volume single-name CDS cleared at JSCC climbed by roughly 40% in 2018 against the previous year, hitting ¥1.29 trillion ($137 billion) in notional by the year end. As Risk.net reported in June, a large part of that is coming from cleared CDS referencing one name: Softbank.
However, there is now undoubtedly a growing demand for CDS at JSCC due to the introduction of accounting CVA at the Japanese megabanks. A director of a recently established XVA desk at one of the banks says they have been clearing all of their CDS trades at JSCC over the past three months.
"We are probably the biggest player in this market and all of our CDS trades are cleared at the JSCC," he says. "I think they are doing very well. For us there are no issues, no stress."
Overall single-name CDSs in Japan are notoriously illiquid, but Hosomura believes the addition of 13 more names at JSCC in October 2018 will over time contribute to improved liquidity in the market.
"Japanese banks introducing CVA liquidity in the Japanese CDS market is going to be very important," says Hosomura. "Index clearing volume has almost tripled in the second quarter compared to the last year. Further, the Japanese megabanks asked JSCC to expand the number of eligible single-names, which they need for their CDS hedges. As this will encourage participation in CDS in the Japanese market, we have high expectations that liquidity will build over the coming year."
Japan Securities Clearing Corporation (JSCC)
Profit & loss (P&L)