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tokens: survey volume between the models, the luminosity functions agree well for magnitudes brighter than M UV = −16 mag. This is essentially due to the fact that for relatively small amplification factors, the variations of the amplification and the effective volume are anticorrelated and tend to maintain the same faint-end slope. Furthermore, relative errors on the effective volume are smaller than what is observed at M UV > −16 mag. Figure 10. Impact of cluster mass models on the UV luminosity function in MACS1149: Left: the effective survey volume (and associated 1σ uncertainties) as a function of absolute intrinsic magnitude for each lensing model. The effective volume depends mainly on the geometrical configuration, the magnification map, and the surface area (cf. Fig 9 ) of each model. Right: Derived UV luminosity function using each cluster model and associated uncertainties. All models support a turnover in the faint-end of the LF but at different magnitudes and with different slopes. Beyond an absolute magnitude of M UV = −15 mag, the differences are too large to robustly constrain the LF shape. In the next section, we use the same procedure in each cluster to assess the lensing uncertainties in the final LF that combines the six clusters. 7.3 Final constraints on the UV LF We now compute the final UV luminosity function at ∼6 resulting from the combination of all the individual clusters. In order to account for all the uncertainties described in the previous sections, we use MCMC simulations to explore the full error space of each galaxy and each effective volume in a given magnitude bin. At the level of an individual galaxy, the MCMC simulations take into account the magnification factor uncertainties by using multiple lensing models. Similarly to the previous section, we randomly sample the completeness functions and lensing models shown in the left-hand side of Fig. 10 for the corresponding cluster. For each galaxy magnitude, the MCMC procedure randomly samples the four effective volumes, which are based on four different lensing models, and their associated uncertainties. For each iteration, the total sample is distributed in magnitude bins M i , with a size of 0.5 mag, for which φ( M i ) is computed with the associated cosmic variance errors estimated from the = 7 lensing curve in fig. 2 of Robertson et al. ( 2013 ). These simulations have the advantage of accounting for uncertainties in M i when estimating uncertainties on φ( M i ) because individual galaxies can change bins at each iteration, and at the same time, rectifying the limitations of the binning procedure. Simultaneously, we also fit the resulting LF results at each iteration to determine the likelihood of the modified Schechter parameters. We here combine our results with the bright-end constraints derived by Bouwens et al. ( 2015 ) from wide-area blank field surveys. To account for the difference in the redshift selection function between their = 7 sample and our combined