7 observations by taking the Fast Fourier Transform after multiplying the images by the corresponding primary beam. Additionally, we leave as free parameters the disc inclination ($i$), position angle (PA), RA and Dec offsets for the three observation sets, and a disc spectral index (${\alpha_\mathrm{mm}}$) that sets the flux at 0.86 mm given the dust mass and opacity at 1.1 mm, i.e. the size distribution is assumed to be the same throughout the disc. In total, our model has 19 parameters, 10 for the density distribution and 9 for the disc centre, orientation, and spectral index.

To find the best fit parameters we sample the parameter space using the <span style="font-variant:small-caps;">PYTHON</span> module <span style="font-variant:small-caps;">emcee</span>, which implements Goodman & Weare’s Affine Invariant MCMC Ensemble sampler [@GoodmanWeare2010; @emcee]. The posterior probability distribution is defined as the product of the likelihood function (proportional to $\exp[-\chi^{2}/2]$) and prior distributions which we assume uniform, although we impose a lower limit for $h$ of 0.03 due to model resolution constraints. In computing the $\chi^2$ over the three visibility sets we applied three constant re-weighting factors for band 6, 12m band 7 and for ACA band 7 that ensures that the final reduced $\chi^2$ of each of 