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. It necessitates no new elements besides those used for the
prediction of 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 $\Delta z
\rightarrow 0$. 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 recovers nicely the signal-to-noise ratio as
well as Fisher forecasts on cosmological parameters of the $w$CDM cosmological
model. Moreover it allows for a fast prediction of which parameters are going
to be the most affected by SSC and at which level. In the case of photometric
galaxy clustering with Euclid-like specifications, we find that $\sigma_8$,
$n_s$ 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 needs
be. We release publicly a Python module allowing to implement the SSC
approximation, as well as a notebook reproducing the plots of the article, at
https://github.com/fabienlacasa/PySSC
