Title: De-blending Deep Herschel Surveys: A Multi-wavelength Approach,
Abstract: Cosmological surveys in the far infrared are known to suffer from confusion.
The Bayesian de-blending tool, XID+, currently provides one of the best ways to
de-confuse deep Herschel SPIRE images, using a flat flux density prior. This
work is to demonstrate that existing multi-wavelength data sets can be
exploited to improve XID+ by providing an informed prior, resulting in more
accurate and precise extracted flux densities. Photometric data for galaxies in
the COSMOS field were used to constrain spectral energy distributions (SEDs)
using the fitting tool CIGALE. These SEDs were used to create Gaussian prior
estimates in the SPIRE bands for XID+. The multi-wavelength photometry and the
extracted SPIRE flux densities were run through CIGALE again to allow us to
compare the performance of the two priors. Inferred ALMA flux densities
(F$^i$), at 870$\mu$m and 1250$\mu$m, from the best fitting SEDs from the
second CIGALE run were compared with measured ALMA flux densities (F$^m$) as an
independent performance validation. Similar validations were conducted with the
SED modelling and fitting tool MAGPHYS and modified black body functions to
test for model dependency. We demonstrate a clear improvement in agreement
between the flux densities extracted with XID+ and existing data at other
wavelengths when using the new informed Gaussian prior over the original
uninformed prior. The residuals between F$^m$ and F$^i$ were calculated. For
the Gaussian prior, these residuals, expressed as a multiple of the ALMA error
($\sigma$), have a smaller standard deviation, 7.95$\sigma$ for the Gaussian
prior compared to 12.21$\sigma$ for the flat prior, reduced mean, 1.83$\sigma$
compared to 3.44$\sigma$, and have reduced skew to positive values, 7.97
compared to 11.50. These results were determined to not be significantly model
dependent. This results in statistically more reliable SPIRE flux densities.