using , following approaches similar to those described in @Koekemoer2002 [@koekemoer_2011; @koekemoer_2013], with a linear pixel scale of $0\farcs{}06$ (a spatial scale of $\approx 0.36$ kpc at $z\simeq6$) and a `pixfrac` parameter of 0.8, to reduce correlated noise while maintaining a relatively uniform weighting per-pixel. We used “ERR” (inverse variance) weighting for the final image combination step. We transformed the ERR extensions from the HST exposures to per-pixel RMS error maps that include all sources of error, including shot noise, and account for correlated noise, as in @Casertano2000 and @Dickinson2004, using [^2].

Source Modeling and Point Source Subtraction
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![image](figures/CFHQS-J0033-0125.pdf){width="94.00000%"}

![image](figures/SDSS-J0129-0035.pdf){width="94.00000%"} ![image](figures/SDSS-J0203+0012.pdf){width="94.00000%"} ![image](figures/NDWFS-J1425+3254.pdf){width="94.00000%"}

![image](figures/SDSS-J2054-0005.pdf){width="94.00000%"} ![image](figures/SDSS-J0005-0006.pdf){width="94.00000%"}

We performed 2D surface brightness modeling for each quasar using the publicly available MCMC-based software [@Mechtley2014; @Mechtley2016]. allows the user to model an input image using a combination of point sources and profiles [@Sersic1963; @Sersic1968] with the parameters: sky background; point source magnitude and position; and magnitude, position, index $n$, effective radius of the major axis $R_e$, ratio between the major and minor axes $b/a$, and position angle. The MCMC process explores a range of model parameters specified by input prior probability distributions, convolving each model with an input PSF and comparing it with the 