Euclid preparation

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Abstract
The European Space Agency’s Euclid mission will observe approximately 14000 deg2 of the extragalactic sky and deliver high-quality imaging of a large number of galaxies. The depth and high spatial resolution of the data will enable a detailed analysis of the stellar population properties of local galaxies through spatially resolved spectral energy distribution (SED) fitting. In this study, we test our pipeline for spatially resolved SED fitting using synthetic images of Euclid, LSST, and GALEX generated from the TNG50 simulation using the SKIRT 3D radiative transfer code. Our pipeline uses functionalities in piXedfit for processing the simulated data cubes and carrying out SED fitting. We apply our pipeline to 25 simulated galaxies at z ∼ 0 to recover their resolved stellar population properties. For each galaxy, we produce three types of data cubes: GALEX + LSST + Euclid, LSST + Euclid, and Euclid-only. We performed the SED fitting tests with two stellar population synthesis (SPS) models in a Bayesian framework. Because the age, metallicity (Z), and dust attenuation estimates are biased when applying only classical formulations of flat priors (even with the combined GALEX + LSST + Euclid data), we examined the effects of additional physically motivated priors in the forms of mass-age and mass-metallicity relations, constructed using a combination of empirical and simulated data. Stellar-mass surface densities can be recovered well using any of the three data cubes, regardless of the SPS model and prior variations. The new priors then significantly improve the measurements of mass-weighted age and Z compared to results obtained without priors, but they may play an excessive role compared to the data in determining the outcome when no ultraviolet (UV) data is available. Compared to varying the spectral extent of the data cube or including and discarding the additional priors, replacing one SPS model family with the other has little effect on the results. The spatially resolved SED fitting method is powerful for mapping the stellar population properties of many galaxies with the current abundance of high-quality imaging data. Our study re-emphasizes the gain added by including multi-wavelength data from ancillary surveys and the roles of priors in Bayesian SED fitting. With the Euclid data alone, we will be able to generate complete and deep stellar mass maps of galaxies in the local Universe (z ≲ 0.1), exploiting the telescope’s wide field, near-infrared sensitivity, and high spatial resolution.

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