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tokens: the galaxy power spectrum in order to arrive at predicted BAO constraints. The previous Euclid forecasts ( Amendola et al. 2013 ) are currently being updated with the new HLFs and updated instrument parameters, and we anticipate that the public documents will be updated soon. The HLFs presented here have already been incorporated in the most recent WFIRST-AFTA science report ( Spergel et al. 2015b ). Appendix A: Cosmic variance This Appendix describes the treatment of cosmic variance in the Model 3 fits. In the linear regime, the cosmic variance error covariance between two luminosity function bins and coming from the matter density field is given by where is the number of independent fields, is the real-space matter power spectrum at redshift , is the growth rate (which boosts the cosmic variance in narrow-band surveys due to redshift-space distortions), and is the window function, the Fourier transform of the survey volume, normalized to . We have used this result here assuming a bias of (from a semianalytic model, Orsi et al. 2010 , although there is evidence that the bias of star-forming galaxies might be higher; see e.g. Geach et al. 2012 ). This reduces the cosmic variance matrix to (Here all the entries in the covariance are constant.) The HiZELS error bars do not incorporate a contribution from cosmic variance. However, we can estimate it from Eq. ( A.1 ) assuming the geometry of independent boxes of size deg each. The depth in the radial direction is given by the width of the narrow-band filter, and is , 0.030, 0.032, and 0.032 at , 0.84, 1.47, and 2.23 respectively. The faintest bins in HIZELS at come from the HAWK-I camera, and the survey volume is smaller in this case: it is a single field, with size deg, and width . For the 4 redshift bins and the luminosity function bins where the full field has been observed, the implied diagonal elements of the covariance are 0.100, 0.045, 0.032, and 0.025. For the HAWK-I data (faintest objects at ), we find a variance of 0.256. A more subtle issue is that the above procedure assumes that the bias is independent of . This assumption has been commonly used for the purpose of forecasting H survey performance and its dependence on survey design. However, in combination with Eq. ( A.1 ), it implies that the cosmic variance contributions in each bin are perfectly correlated. That means that a fit using Eq. ( A.1 ) will assume that the shape of the HLF has no cosmic variance: the cosmic variance term will instead allow only the normalization to float up and down with an uncertainty given by Eq. ( A.1 ). Since cosmic variance is the largest contributor to the errors in some luminosity ranges, the procedure above could lead to fit results that are artificially well-constrained, if the bias is in fact dependent on . There is no reason for to be exactly zero, although for star-forming galaxies it is not obvious which sign to expect. We have thus explored the possibility of averaging the covariance matrix