Non-parametric decompositions of disk galaxies in S${^4}$G using DiskFit
  We present photometric models of 532 disk galaxies in 3.6{\mu}m images from
the Spitzer Survey of Stellar Structure in Galaxies (S$^4$G) using the
non-parametric DiskFit algorithm. We first test DiskFit's performance on 400
synthetic S$^4$G-like galaxy images. DiskFit is unreliable in the bulge region,
but accurately disentangles exponential disks from Ferrers bars farther out as
long as their position angles differ by more than 5${^\circ}$. We then proceed
to model the S$^4$G galaxies, successfully fitting 489 of them using an
automated approach for initializing DiskFit, optimizing the model and deriving
uncertainties using a bootstrap-resampling technique. The resulting component
geometries and surface brightness profiles are compared to those derived by
Salo et al. (2015) using the parametric model galfit. We find generally good
agreement between the models, but discrepancies between best-fitting values for
individual systems are often significant: the choice of algorithm clearly
impacts the inferred disk and bar structure. In particular, we find that
DiskFit typically assigns more light to the bar and less light to the disk
relative to the Ferrers and exponential profiles presented by Salo et al.
(2015) in the bar region. Given DiskFit's reliability at disentangling these
components in our synthetic images, we conclude that the surface brightness
distributions of barred S$^4$G galaxies are not well-represented by these
functional forms. The results presented here underscore the importance of
validating photometric decomposition algorithms before applying them to real
data and the utility of DiskFit's non-parametric approach at measuring the
structure of disks and bars in nearby galaxies.
