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Narrow-band imaging surveys allow the study of the spectral characteristicsof galaxies without the need of performing their spectroscopic follow-up. Inthis work, we forward-model the Physics of the Accelerating Universe Survey(PAUS) narrow-band data. The aim is to improve the constraints on the spectralcoefficients used to create the galaxy spectral energy distributions (SED) ofthe galaxy population model in Tortorelli et al. 2020. In that work, the modelparameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey(CFHTLS) data using Approximate Bayesian Computation (ABC). This led tostringent constraints on the B-band galaxy luminosity function parameters, butleft the spectral coefficients only broadly constrained. To address that, weperform an ABC inference using CFHTLS and PAUS data. This is the first time ourapproach combining forward-modelling and ABC is applied simultaneously tomultiple datasets. We test the results of the ABC inference by comparing thenarrow-band magnitudes of the observed and simulated galaxies using PrincipalComponent Analysis, finding a very good agreement. Furthermore, we prove thescientific potential of the constrained galaxy population model to providerealistic stellar population properties by measuring them with the SED fittingcode \textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometryfor a flux-limited ($\mathrm{i}<22.5$) sample of galaxies up to redshift$\mathrm{z \sim 0.8}$. We find that properties like stellar masses,star-formation rates, mass-weighted stellar ages and metallicities are inagreement within errors between observations and simulations. Overall, thiswork shows the ability of our galaxy population model to correctlyforward-model a complex dataset such as PAUS and the ability to reproduce thediversity of galaxy properties at the redshift range spanned by CFHTLS andPAUS.

The PAU Survey: Measurement of Narrow-band galaxy properties with Approximate Bayesian Computation