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ABSTRACT We present a map of the total intrinsic reddening across ≃34 deg2 of the Small Magellanic Cloud (SMC) derived using optical (ugriz) and near-infrared (IR; YJKs) spectral energy distributions (SEDs) of background galaxies. The reddening map is created using a subsample of 29 274 galaxies with low levels of intrinsic reddening based on the lephare χ2 minimization SED-fitting routine. We find statistically significant enhanced levels of reddening associated with the main body of the SMC compared with regions in the outskirts [ΔE(B − V) ≃ 0.3 mag]. A comparison with literature reddening maps of the SMC shows that, after correcting for differences in the volume of the SMC sampled, there is good agreement between our results and maps created using young stars. In contrast, we find significant discrepancies between our results and maps created using old stars or based on longer wavelength far-IR dust emission that could stem from biased samples in the former and uncertainties in the far-IR emissivity and the optical properties of the dust grains in the latter. This study represents one of the first large-scale categorizations of extragalactic sources behind the SMC and as such we provide the lephare outputs for our full sample of ∼500 000 sources. 

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown.Euclidis a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg²of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.