Super-sample Covariance Research Articles

Towards including super-sample covariance in the unbinned likelihood for cluster abundance cosmology

ABSTRACT The measurement of the abundance of galaxy clusters in the Universe is a sensitive probe of cosmology, which depends on both the expansion history of the Universe and the growth of structure. Density fluctuations across the finite survey volume add noise to this measurement, this is often referred to as super-sample covariance (SSC). For an unbinned cluster analysis, such noise has not been included in the cluster likelihood, since the effect of SSC was small compared to the Poisson shot-noise for samples of a few hundred clusters. For upcoming large cluster surveys such as the Rubin LSST, which will deliver catalogues of tens of thousands of clusters, this effect will no longer be negligible. In this paper, we propose a new hybrid likelihood based on the Gauss-Poisson Compound model (GPC), by using infinitesimal mass bins and standard redshift bins. This likelihood has the advantages of an unbinned Poisson likelihood while successfully incorporating the effects of SSC. Using a simulated dark matter halo catalogue, we find that the hybrid likelihood, accounting for both Poisson noise and SSC, increases the dispersion of the parameter posteriors by 20 per cent when using 100 000 clusters compared to the standard unbinned likelihood, based on Poisson statistics only.

What is the super-sample covariance? A fresh perspective for second-order shear statistics

Cosmological analyses of second-order weak lensing statistics require precise and accurate covariance estimates. These covariances are impacted by two sometimes neglected terms: a negative contribution to the Gaussian covariance due to a finite survey area, and the super-sample covariance (SSC), which for the power spectrum contains the impact of Fourier modes larger than the survey window. We show here that these two effects are connected and can be seen as correction terms to the ‘large-field-approximation’, the asymptotic case of an infinitely large survey area. We describe the two terms collectively as “finite-field terms”. We derive the covariance of second-order shear statistics from first principles. For this, we use an estimator in real space without relying on an estimator for the power spectrum. The resulting covariance does not scale inversely with the survey area, as might naively be assumed. This scaling is only correct under the large-field approximation when the contribution of the finite-field terms tends to zero. Furthermore, all parts of the covariance, not only the SSC, depend on the power spectrum and trispectrum at all modes, including those larger than the survey. We also show that it is generally impossible to transform an estimate of the power spectrum covariance into the covariance of a real-space statistic. Such a transformation is only possible in the asymptotic case of the large-field approximation. Additionally, we find that the total covariance of a real-space statistic can be calculated using correlation function estimates on spatial scales smaller than the survey window. Consequently, estimating covariances of real-space statistics, in principle, does not require information on spatial scales larger than the survey area. We demonstrate that this covariance estimation method is equivalent to the standard sample covariance method.

CSST WL preparation I: forecast the impact from non-Gaussian covariances and requirements on systematics control

ABSTRACT The precise estimation of the statistical errors and accurate removal of the systematical errors are the two major challenges for the stage IV cosmic shear surveys. We explore their impact for the China Space Station Telescope (CSST) with survey area ${\sim} 17\,500\deg ^2$ up to redshift ∼4. We consider statistical error contributed from Gaussian covariance, connected non-Gaussian covariance, and super-sample covariance. We find the non-Gaussian covariances, which is dominated by the super-sample covariance, can largely reduce the signal-to-noise ratio of the two-point statistics for CSST, leading to an ∼1/3 loss in the figure of merit for the matter clustering properties (σ8–Ωm plane) and 1/6 in the dark energy equation of state (w0–wa plane). We further put requirements of systematics mitigation on intrinsic alignment of galaxies, baryonic feedback, shear multiplicative bias, and bias in the redshift distribution, for an unbiased cosmology. The 10−2–10−3 level requirements emphasize strong needs in related studies, to support future model selections and the associated priors for the nuisance parameters.

Super-sample covariance of the power spectrum, bispectrum, halos, voids, and their cross covariances

Super-sample covariance of the power spectrum, bispectrum, halos, voids, and their cross covariances

Efficient computation of the super-sample covariance for stage IV galaxy surveys

Super-sample covariance (SSC) is an important effect for cosmological analyses that use the deep structure of the cosmic web; it may, however, be nontrivial to include it practically in a pipeline. We solve this difficulty by presenting a formula for the precision (inverse covariance) matrix and show applications to update likelihood or Fisher forecast pipelines. The formula has several advantages in terms of speed, reliability, stability, and ease of implementation. We present an analytical application to show the formal equivalence between three approaches to SSC: (i) at the usual covariance level, (ii) at the likelihood level, and (iii) with a quadratic estimator. We then present an application of this computationally efficient framework for studying the impact of inaccurate modelling of SSC responses for cosmological constraints from stage IV surveys. We find that a weak-lensing-only analysis is very sensitive to inaccurate modelling of the scale dependence of the response, which needs to be calibrated at the ∼15% level. The sensitivity to this scale dependence is less severe for the joint weak-lensing and galaxy clustering analysis (also known as 3×2pt). Nevertheless, we find that both the amplitude and scale-dependence of the responses have to be calibrated at better than 30%.

It takes two to know one: computing accurate one-point PDF covariances from effective two-point PDF models

One-point probability distribution functions (PDFs) of the cosmic matter density are powerful cosmological probes that extract non-Gaussian properties of the matter distribution and complement two-point statistics. Computing the covariance of one-point PDFs is key for building a robust galaxy survey analysis for upcoming surveys like Euclid and the Rubin Observatory LSST and requires good models for the two-point PDFs characterising spatial correlations. In this work, we obtain accurate PDF covariances using effective shifted lognormal two-point PDF models for the mildly non-Gaussian weak lensing convergence and validate our predictions against large sets of Gaussian and non-Gaussian maps. We show how the dominant effects in the covariance matrix capturing super-sample covariance arise from a large-separation expansion of the two-point PDF and discuss differences between the covariances obtained from small patches and full sky maps. Finally, we describe how our formalism can be extended to characterise the PDF covariance for 3D-dimensional spectroscopic fields using the 3D matter PDF as an example. We describe how covariances from simulated boxes with fixed overall density can be supplemented with the missing super-sample covariance effect by relying on theoretical predictions validated against separate-universe style simulations.

Sample variance for supernovae distance measurements and the Hubble tension

Recent local measurements of the Hubble constant made using supernovae have delivered a value that differs by $\ensuremath{\sim}5\ensuremath{\sigma}$ (statistical error) from predictions using the cosmic microwave background (CMB), or using baryon acoustic oscillations (BAO) and big-bang nucleosynthesis (BBN) constraints, which are themselves consistent. The effective volume covered by the supernovae is small compared to the other probes, and it is therefore interesting to consider whether sample variance (often also called cosmic variance) is a significant contributor to the offset. We consider four ways of calculating the sample variance: (i) perturbation theory applied to the luminosity distance, which is the most common method considered in the literature; (ii) perturbation of cosmological parameters, as is commonly used to alleviate supersample covariance in sets of N-body simulations; (iii) a new method based on the variance between perturbed spherical top-hat regions; (iv) using numerical N-body simulations. All give consistent results showing that, for the Pantheon supernova sample, sample variance can only lead to fluctuations in ${H}_{0}$ of order $\ifmmode\pm\else\textpm\fi{}1\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$ or less. While this is not in itself a new result, the agreement between the methods used adds to its robustness. Furthermore, it is instructive to see how the different methods fit together. We also investigate the internal variance of the ${H}_{0}$ measurement using SH0ES and Pantheon data. By searching for an offset between measurements in opposite hemispheres, we find that the direction coincident with the CMB dipole has a higher ${H}_{0}$ measurement than the opposite hemisphere by roughly $4\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$. We compare this with a large number of simulations and find that the size of this asymmetry is statistically likely, but the preference of direction may indicate that further calibration is needed.

Euclid: Covariance of weak lensing pseudo-Cℓ estimates

An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-Cℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N-body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.

Impact of survey geometry and super-sample covariance on future photometric galaxy surveys

Photometric galaxy surveys probe the late-time Universe where the density field is highly non-Gaussian. A consequence is the emergence of the super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive to fluctuations on scales larger than the survey window. In this work, we study the impact of the survey geometry on the SSC and, subsequently, on cosmological parameter inference. We devise a fast SSC approximation that accounts for the survey geometry and compare its performance to the common approximation of rescaling the results by the fraction of the sky covered by the survey, fSKY, dubbed ‘full-sky approximation’. To gauge the impact of our new SSC recipe, that we call ‘partial-sky’, we perform Fisher forecasts on the parameters of the (w0, wa)-CDM model in a 3 × 2 point analysis, varying the survey area, the geometry of the mask, and the galaxy distribution inside our redshift bins. The differences in the marginalised forecast errors –with the full-sky approximation performing poorly for small survey areas but excellently for stage-IV-like areas– are found to be absorbed by the marginalisation on galaxy bias nuisance parameters. For large survey areas, the unmarginalised errors are underestimated by about 10% for all probes considered. This is a hint that, even for stage-IV-like surveys, the partial-sky method introduced in this work will be necessary if tight priors are applied on these nuisance parameters. We make the partial-sky method public with a new release of the public code PySSC.

Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids

Abstract We quantify the information content of the nonlinear matter power spectrum, the halo mass function, and the void size function, using the Quijote N-body simulations. We find that these three statistics exhibit very different degeneracies among the cosmological parameters, and thus the combination of all three probes enables the breaking of degeneracies, in turn yielding remarkably tight constraints. We perform a Fisher analysis using the full covariance matrix, including all auto- and cross correlations, finding that this increases the information content for neutrino mass compared to a correlation-free analysis. The multiplicative improvement of the constraints on the cosmological parameters obtained by combining all three probes compared to using the power spectrum alone are: 137, 5, 8, 20, 10, and 43, for Ω m , Ω b , h, n s , σ 8, and M ν , respectively. The marginalized error on the sum of the neutrino masses is σ(M ν ) = 0.018 eV for a cosmological volume of 1 h − 1 Gpc 3 , using k max = 0.5 h Mpc − 1 , and without cosmic microwave background (CMB) priors. We note that this error is an underestimate insomuch as we do not consider super-sample covariance, baryonic effects, and realistic survey noises and systematics. On the other hand, it is an overestimate insomuch as our cuts and binning are suboptimal due to restrictions imposed by the simulation resolution. Given upcoming galaxy surveys will observe volumes spanning ∼ 100 h − 1 Gpc 3 , this presents a promising new avenue to measure neutrino mass without being restricted by the need for accurate knowledge of the optical depth, which is required for CMB-based measurements. Furthermore, the improved constraints on other cosmological parameters, notably Ω m , may also be competitive with CMB-based measurements.

Super sample covariance of the thermal Sunyaev-Zel’dovich effect

The thermal Sunyaev-Zel'dovich (tSZ) effect is a powerful probe of cosmology. The statistical errors in the tSZ power spectrum measurements are dominated by the presence of massive clusters in a survey volume that are easy to identify on individual cluster basis. First, we study the impact of super sample covariance (SSC) on the tSZ power spectrum measurements, and find that the sample variance is dominated by the connected non-Gaussian (cNG) covariance arising mainly from Poisson number fluctuations of massive clusters in the survey volume. Second, we find that removing such individually-detected, massive clusters from the analysis significantly reduces the cNG contribution, thereby leading the SSC to be a leading source of the sample variance. We then show, based on Fisher analysis, that the power spectrum measured from the remaining diffuse tSZ effects can be used to obtain tight constraints on cosmological parameters as well as the hydrostatic mass bias parameter. Our method offers complementary use of individual tSZ cluster counts and the power spectrum measurements of diffuse tSZ signals for cosmology and intracluster gas physics.

Cosmological constraints from BOSS with analytic covariance matrices

We use analytic covariance matrices to carry out a full-shape analysis of the galaxy power spectrum multipoles from the Baryon Oscillation Spectroscopic Survey (BOSS). We obtain parameter estimates that agree well with those based on the sample covariance from two thousand galaxy mock catalogs, thus validating the analytic approach and providing substantial reduction in computational cost. We also highlight a number of additional advantages of analytic covariances. First, the analysis does not suffer from sampling noise, which biases the constraints and typically requires inflating parameter error bars. Second, it allows us to study convergence of the cosmological constraints when recomputing the analytic covariances to match the best-fit power spectrum, which can be done at a negligible computational cost, unlike when using mock catalogs. These effects reduce the systematic error budget of cosmological constraints, which suggests that the analytic approach may be an important tool for upcoming high-precision galaxy redshift surveys such as DESI and Euclid. Finally, we study the impact of various ingredients in the power spectrum covariance matrix and show that the non-Gaussian part, which includes the regular trispectrum and super-sample covariance, has a marginal effect ($\lesssim 10 \%$) on the cosmological parameter error bars. We also suggest improvements to analytic covariances that are commonly used in Fisher forecasts.

Galaxy power spectrum multipoles covariance in perturbation theory

We compute the covariance of the galaxy power spectrum multipoles in perturbation theory, including the effects of nonlinear evolution, nonlinear and nonlocal bias, radial redshift-space distortions, arbitrary survey window and shot noise. We rewrite the power spectrum FKP estimator in terms of the usual windowed galaxy fluctuations and the fluctuations in the number of galaxies inside the survey volume. We show that this leads to a stronger super-sample covariance than assumed in the literature and causes a substantial leakage of Gaussian information. We decompose the covariance matrix into several contributions that provide an insight into its behavior for different biased tracers. We show that for realistic surveys, the covariance of power spectrum multipoles is already dominated by shot noise and super survey mode coupling in the weakly non-linear regime. Both these effects can be accurately modeled analytically, making a perturbative treatment of the covariance very compelling. Our method allows for the covariance to be varied as a function of cosmology and bias parameters very efficiently, with survey geometry entering as fixed kernels that can be computed separately using fast fourier transforms (FFTs). We find excellent agreement between our analytic covariance and that estimated from BOSS DR12 Patchy mock catalogs in the whole range we tested, up to $k=0.6$ h/Mpc. This bodes well for application to future surveys such as DESI and Euclid. The CovaPT code that accompanies this paper is available at https://github.com/JayWadekar/CovaPT

Power spectrum of halo intrinsic alignments in simulations

ABSTRACT We use a suite of N-body simulations to study intrinsic alignments (IA) of halo shapes with the surrounding large-scale structure in the ΛCDM model. For this purpose, we develop a novel method to measure multipole moments of the three-dimensional power spectrum of the E-mode field of halo shapes with the matter/halo distribution, $P_{\delta E}^{(\ell)}(k)$ (or $P^{(\ell)}_{{\rm h}E}$), and those of the auto-power spectrum of the E-mode, $P^{(\ell)}_{EE}(k)$, based on the E/B-mode decomposition. The IA power spectra have non-vanishing amplitudes over the linear to non-linear scales, and the large-scale amplitudes at k ≲ 0.1 h−1 Mpc are related to the matter power spectrum via a constant coefficient (AIA), similar to the linear bias parameter of galaxy or halo density field. We find that the cross- and auto-power spectra PδE and PEE at non-linear scales, k ≳ 0.1 h−1 Mpc, show different k-dependences relative to the matter power spectrum, suggesting a violation of the non-linear alignment model commonly used to model contaminations of cosmic shear signals. The IA power spectra exhibit baryon acoustic oscillations, and vary with halo samples of different masses, redshifts, and cosmological parameters (Ωm, S8). The cumulative signal-to-noise ratio for the IA power spectra is about 60 per cent of that for the halo density power spectrum, where the super-sample covariance is found to give a significant contribution to the total covariance. Thus our results demonstrate that the IA power spectra of galaxy shapes, measured from imaging and spectroscopic surveys for an overlapping area of the sky, can be used to probe the underlying matter power spectrum, the primordial curvature perturbations, and cosmological parameters, in addition to the standard galaxy density power spectrum.

Cosmological consequences of intrinsic alignments supersample covariance

ABSTRACT We examine cosmological constraints from high-precision weak-lensing surveys including supersample covariance (SSC) due to the finite survey volume. Specifically, we focus on the contribution of beat coupling in the intrinsic alignments as a part of full cosmic shear signal under flat-sky approximation. The SSC-effect grows by going to lower redshift bin and indicates considerable footprint on the intermediate and high multipoles for cumulative signal-to-noise ratio (SNR). The SNR is reduced by $\approx 10{{\ \rm per\ cent}}$ as a consequence of including the intrinsic alignment SSC, for the full cosmic shear signal, depending on the amplitude of intrinsic alignments, the ellipticity dispersion, and the survey redshift ranges, while the contribution of photometric redshift error can be ignored in the cumulative SNR. Using the Fisher-matrix formalism, we find that the impact of large modes beyond the volume of the surveys on the small modes alters the intrinsic alignments. However, corresponding impact on the cosmological parameters’ estimation is marginal compared to that of for gravitational weak lensing, particularly, when all available redshift bins are considered. Our results also demonstrate that including SSC-effect on the intrinsic alignments in the analytical covariance matrix of full cosmic shear leads to increase marginally the confidence interval for σ8 by $\approx 10{{\ \rm per\ cent}}$ for a sample with almost high intrinsic alignment amplitude.

Covariance of the redshift-space matter power spectrum after reconstruction

We explore the covariance of redshift-space matter power spectra after a standard density-field reconstruction. We derive perturbative formula of the covariance at the tree-level order and find that the amplitude of the off-diagonal components from the trispectrum decreases by reconstruction. Using a large set of N-body simulations, we also find the similar reduction of the off-diagonal components of the covariance and thereby the signal-to-noise ratio (S/N) of the post-reconstructed (post-rec) power spectra significantly increases compared to the pre-reconstructed spectra. This indicates that the information leaking to higher-order statistics come back to the two-point statistics by reconstruction. Interestingly, the post-rec spectra have higher S/N than the linear spectrum with Gaussian covariance when the scale of reconstruction characterized with the smoothing scale of the shift field is below ~10Mpc/h where the trispectrum becomes negative. We demonstrate that the error of the growth rate estimated from the monopole and quadrupole components of the redshift-space matter power spectra significantly improves by reconstruction. We also find a similar improvement of the growth rate even when taking into account the super-sample covariance, while the reconstruction cannot correct for the field variation of the super-sample modes.

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Context. Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed. Aims. In this work, we evaluate the impact of the reduced shear approximation and magnification bias on information obtained from the angular power spectrum. To first-order, the statistics of reduced shear, a combination of shear and convergence, are taken to be equal to those of shear. However, this approximation can induce a bias in the cosmological parameters that can no longer be neglected. A separate bias arises from the statistics of shear being altered by the preferential selection of galaxies and the dilution of their surface densities in high-magnification regions. Methods. The corrections for these systematic effects take similar forms, allowing them to be treated together. We calculated the impact of neglecting these effects on the cosmological parameters that would be determined from Euclid, using cosmic shear tomography. To do so, we employed the Fisher matrix formalism, and included the impact of the super-sample covariance. We also demonstrate how the reduced shear correction can be calculated using a lognormal field forward modelling approach. Results. These effects cause significant biases in Ωm, σ8, ns, ΩDE, w0, and wa of −0.53σ, 0.43σ, −0.34σ, 1.36σ, −0.68σ, and 1.21σ, respectively. We then show that these lensing biases interact with another systematic effect: the intrinsic alignment of galaxies. Accordingly, we have developed the formalism for an intrinsic alignment-enhanced lensing bias correction. Applying this to Euclid, we find that the additional terms introduced by this correction are sub-dominant.

The impact of braiding covariance and in-survey covariance on next-generation galaxy surveys

As galaxy surveys improve their precision thanks to lower levels of noise and the push toward small, non-linear scales, the need for accurate covariances beyond the classical Gaussian formula becomes more acute. Here I investigate the analytical implementation and impact of non-Gaussian covariance terms that I had previously derived for the galaxy angular power spectrum. Braiding covariance is such an interesting class of such terms and it gets contributions both from in-survey and super-survey modes, the latter proving difficult to calibrate through simulations. I present an approximation for braiding covariance which speeds up the process of numerical computation. I show that including braiding covariance is a necessary condition for including other non-Gaussian terms, namely the in-survey 2-, 3-, and 4-halo covariance. Indeed these terms yield incorrect covariance matrices with negative eigenvalues if considered on their own. I then move to quantify the impact on parameter constraints, with forecasts for a survey with Euclid-like galaxy density and angular scales. Compared with the Gaussian case, braiding and in-survey covariances significantly increase the error bars on cosmological parameters, in particular by 50% for the dark energy equation of state w. The error bars on the halo occupation distribution (HOD) parameters are also affected between 12% and 39%. Accounting for super-sample covariance (SSC) also increases parameter errors, by 90% for w and between 7% and 64% for HOD. In total, non-Gaussianity increases the error bar on w by 120% (between 15% and 80% for other cosmological parameters) and the error bars on HOD parameters between 17% and 85%. Accounting for the 1-halo trispectrum term on top of SSC, as has been done in some current analyses, is not sufficient for capturing the full non-Gaussian impact: braiding and the rest of in-survey covariance have to be accounted for. Finally, I discuss why the inclusion of non-Gaussianity generally eases up parameter degeneracies, making cosmological constraints more robust for astrophysical uncertainties. I released publicly the data and a Python notebook reproducing the results and plots of the article.

Forecasting super-sample covariance in future weak lensing surveys with SuperSCRAM

The observable universe contains density perturbations on scales larger than any finite volume survey. Perturbations on scales larger than a survey can measure degrade its power to constrain cosmological parameters. The dependence of survey observables such as the weak lensing power spectrum on these long-wavelength modes results in super-sample covariance. Accurately forecasting parameter constraints for future surveys requires accurately accounting for the super-sample effects. If super-sample covariance is in fact a major component of the survey error budget, it may be necessary to investigate mitigation strategies that constrain the specific realization of the long-wavelength modes. We present a Fisher matrix based formalism for approximating the magnitude of super-sample covariance and the effectiveness of mitigation strategies for realistic survey geometries. We implement our formalism in the public code SuperSCRAM: Super-Sample Covariance Reduction and Mitigation. We illustrate SuperSCRAM with an example application, where the modes contributing to super-sample covariance in the WFIRST weak lensing survey are constrained by the low-redshift galaxy number counts in the wider LSST footprint. We find that super-sample covariance increases the volume of the error ellipsoid in 7D cosmological parameter space by a factor of 4.5 relative to Gaussian statistical errors only, but our simple mitigation strategy more than halves the contamination, to a factor of 2.0.

Fast and easy super-sample covariance of large-scale structure observables

We present a numerically cheap approximation to super-sample covariance (SSC) of large-scale structure cosmological probes, first in the case of angular power spectra. No new elements are needed besides those used to predict the considered probes, thus relieving analysis pipelines from having to develop a full SSC modeling, and reducing the computational load. The approximation is asymptotically exact for fine redshift bins Δz → 0. We furthermore show how it can be implemented at the level of a Gaussian likelihood or a Fisher matrix forecast as a fast correction to the Gaussian case without needing to build large covariance matrices. Numerical application to a Euclid-like survey show that, compared to a full SSC computation, the approximation nicely recovers the signal-to-noise ratio and the Fisher forecasts on cosmological parameters of the wCDM cosmological model. Moreover, it allows for a fast prediction of which parameters are going to be the most affected by SSC and at what level. In the case of photometric galaxy clustering with Euclid-like specifications, we find that σ8, ns, and the dark energy equation of state w are particularly heavily affected. We finally show how to generalize the approximation for probes other than angular spectra (correlation functions, number counts, and bispectra) and at the likelihood level, allowing for the latter to be non-Gaussian if necessary. We release publicly a Python module allowing the implementation of the SSC approximation and a notebook reproducing the plots of the article.