The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging
  Weak gravitational lensing is a powerful probe of the dark sector, once
measurement systematic errors can be controlled. In Refregier & Amara (2014), a
calibration method based on forward modeling, called MCCL, was proposed. This
relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture
the key features of galaxy populations and measurement effects. The MCCL
approach has been used in Herbel et al. (2017) to determine the redshift
distribution of cosmological galaxy samples and, in the process, the authors
derived a model for the galaxy population mainly based on broad-band
photometry. Here, we test this model by forward modeling the 40 narrow-band
photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the
same forced photometric pipeline on data and simulations using Source Extractor
(Bertin & Arnouts 1996). The image simulation scheme performance is assessed at
the image and at the catalogues level. We find good agreement for the
distribution of pixel values, the magnitudes, in the magnitude-size relation
and the interband correlations. A principal component analysis is then
performed, in order to derive a global comparison of the narrow-band photometry
between the data and the simulations. We use a `mixing' matrix to quantify the
agreement between the observed and simulated sets of Principal Components
(PCs). We find good agreement, especially for the first three most significant
PCs. We also compare the coefficients of the PCs decomposition. While there are
slight differences for some coefficients, we find that the distributions are in
good agreement. Together, our results show that the galaxy population model
derived from broad-band photometry is in good overall agreement with the PAUS
data. This offers good prospect for incorporating spectral information to the
galaxy model by adjusting it to the PAUS narrow-band data using forward
modeling.
