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Register a new userThe Cosmological Impact of Intrinsic Alignment Model Choice for Cosmic Shear
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We consider the effect of galaxy intrinsic alignments (IAs) on dark energy constraints from weak gravitational lensing. We summarise the latest version of the linear alignment model of IAs, following the brief note of Hirata & Seljak (2010) and further interpretation in Laszlo et al. (2011). We show the cosmological bias on the dark energy equation of state parameters w0 and wa that would occur if IAs were ignored. We find that w0 and wa are both catastrophically biased, by an absolute value of just greater than unity under the Fisher matrix approximation. This contrasts with a bias several times larger for the earlier IA implementation. Therefore there is no doubt that IAs must be taken into account for future Stage III experiments and beyond. We use a flexible grid of IA and galaxy bias parameters as used in previous work, and investigate what would happen if the universe used the latest IA model, but we assumed the earlier version. We find that despite the large difference between the two IA models, the grid flexibility is sufficient to remove cosmological bias and recover the correct dark energy equation of state. In an appendix, we compare observed shear power spectra to those from a popular previous implementation and explain the differences.
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Owing to the mass-sheet degeneracy, cosmic shear maps do not probe directly the Fourier modes of the underlying mass distribution on scales comparable to the survey size and larger. To assess the corresponding effect on attainable cosmological parameter constraints, we quantify the information on super-survey modes in a lognormal model and, when interpreted as nuisance parameters, their degeneracies to cosmological parameters. Our analytical and numerical calculations clarify the central role of super-sample covariance (SSC) in shaping the statistical power of cosmological observables. Reconstructing the background modes from their non-Gaussian statistical dependence to small scales modes yields the renormalized convergence. This diagonalizes the spectrum covariance matrix, and the information content of the corresponding power spectrum is increased by a factor of two over standard methods. Unfortunately, careful calculation of the Cramer-Rao bound shows that the information recovery can never be made complete, any observable built from shear fields, including optimal sufficient statistics, are subject to severe information loss, typically $80%$ to $90%$ below $ell sim 3000$ for generic cosmological parameters. The lost information can only be recovered from additional, non-shear based data. Our predictions hold just as well for a tomographic analysis, and/or full sky surveys.
Developing analysis pipelines based on statistics beyond two-point functions is critical for extracting a maximal amount of cosmological information from current and upcoming weak lensing surveys. In this paper, we study the impact of the intrinsic alignment of galaxies (IA) on three promising probes measured from aperture mass maps -- the lensing peaks, minima and full PDF, in comparison and in combination with the shear two-point correlation functions ($gamma$-2PCFs). Our two-dimensional IA infusion method converts the light-cone-projected mass sheets into projected tidal tensors, which are then linearly coupled to an intrinsic ellipticity component with a strength controlled by the coupling parameter $A_{rm IA}$. We validate our method with the $gamma$-2PCFs statistics, recovering well the analytical calculations from the linear alignment model of citet{BridleKing} in a full tomographic setting, and for different $A_{rm IA}$ values. We next use our method to infuse at the galaxy catalogue level a non-linear IA model that includes the density-weighting term introduced in citet{Blazek2015}, and compute the impact on the three aperture mass map statistics. We find that large snr peaks are maximally affected, with deviations reaching 30% (10%) for a {it Euclid}-like (KiDS-like) survey. Modelling the signal in a $w$CDM cosmology universe with $N$-body simulations, we forecast the cosmological bias caused by unmodelled IA for 100 deg$^2$ of {it Euclid}-like data, finding very large offsets in $w_0$ (5-10$sigma_{rm stat}$), $Omega_{rm m}$ (4-6$sigma_{rm stat}$), and $S_8 equiv sigma_8sqrt{Omega_{rm m}/0.3}$ ($sim$3$sigma_{rm stat}$). The method presented in this paper offers a compelling avenue to account for IA in beyond-two-point weak lensing statistics, with a flexibility comparable to that of current $gamma$-2PCFs IA analytical models.
Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded by the small scales, where the clustering of dark matter and the effects of astrophysical feedback processes are not fully understood. This can bias the estimates of cosmological parameters, which we study here for a joint analysis of mock Euclid cosmic shear and Planck cosmic microwave background data. We use different implementations for the modelling of the signal on small scales and find that they result in significantly different predictions. Moreover, the different nonlinear corrections lead to biased parameter estimates, especially when the analysis is extended into the highly nonlinear regime, with both the Hubble constant, $H_0$, and the clustering amplitude, $sigma_8$, affected the most. Improvements in the modelling of nonlinear scales will therefore be needed if we are to resolve the current tension with more and better data. For a given prescription for the nonlinear power spectrum, using different corrections for baryon physics does not significantly impact the precision of Euclid, but neglecting these correction does lead to large biases in the cosmological parameters. In order to extract precise and unbiased constraints on cosmological parameters from Euclid cosmic shear data, it is therefore essential to improve the accuracy of the recipes that account for nonlinear structure formation, as well as the modelling of the impact of astrophysical processes that redistribute the baryons.
One of the most pernicious theoretical systematics facing upcoming gravitational lensing surveys is the uncertainty introduced by the effects of baryons on the power spectrum of the convergence field. One method that has been proposed to account for these effects is to allow several additional parameters (that characterize dark matter halos) to vary and to fit lensing data to these halo parameters concurrently with the standard set of cosmological parameters. We test this method. In particular, we use this technique to model convergence power spectrum predictions from a set of cosmological simulations. We estimate biases in dark energy equation of state parameters that would be incurred if one were to fit the spectra predicted by the simulations either with no model for baryons, or with the proposed method. We show that neglecting baryonic effect leads to biases in dark energy parameters that are several times the statistical errors for a survey like the Dark Energy Survey. The proposed method to correct for baryonic effects renders the residual biases in dark energy equation of state parameters smaller than the statistical errors. These results suggest that this mitigation method may be applied to analyze convergence spectra from a survey like the Dark Energy Survey. For significantly larger surveys, such as will be carried out by the Large Synoptic Survey Telescope, the biases introduced by baryonic effects are much more significant. We show that this mitigation technique significantly reduces the biases for such larger surveys, but that a more effective mitigation strategy will need to be developed in order ensure that the residual biases in these surveys fall below the statistical errors.
Residual errors in shear measurements, after corrections for instrument systematics and atmospheric effects, can impact cosmological parameters derived from weak lensing observations. Here we combine convergence maps from our suite of ray-tracing simulations with random realizations of spurious shear. This allows us to quantify the errors and biases of the triplet $(Omega_m,w,sigma_8)$ derived from the power spectrum (PS), as well as from three different sets of non-Gaussian statistics of the lensing convergence field: Minkowski functionals (MF), low--order moments (LM), and peak counts (PK). Our main results are: (i) We find an order of magnitude smaller biases from the PS than in previous work. (ii) The PS and LM yield biases much smaller than the morphological statistics (MF, PK). (iii) For strictly Gaussian spurious shear with integrated amplitude as low as its current estimate of $sigma^2_{sys}approx 10^{-7}$, biases from the PS and LM would be unimportant even for a survey with the statistical power of LSST. However, we find that for surveys larger than $approx 100$ deg$^2$, non-Gaussianity in the noise (not included in our analysis) will likely be important and must be quantified to assess the biases. (iv) The morphological statistics (MF,PK) introduce important biases even for Gaussian noise, which must be corrected in large surveys. The biases are in different directions in $(Omega_m,w,sigma_8)$ parameter space, allowing self-calibration by combining multiple statistics. Our results warrant follow-up studies with more extensive lensing simulations and more accurate spurious shear estimates.
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