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. Euclid is 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 deg² of 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.

We show how observations of multiply-imaged quasars at high redshift can be used as a probe of dark matter clumps (subhalos with masses ~ 10^9 solar masses) within the virialized extent of more massive lensing halos. A large abundance of such satellites is predicted by numerical simulations of galaxy formation in cold dark matter (CDM) cosmogonies. Small-scale structure within galaxy halos affects the flux ratios of the images without appreciably changing their positions. We use numerical simulations to quantify the effect of dark matter substructure on the distribution of magnifications, and find that the magnification ratio of a typical image pair will deviate significantly from the value predicted by a smooth lensing potential if, near the Einstein radius, only a few percent of the lens surface density is contained in subhalos. The angular size of the continuum source dictates the range of subclump masses that can have a detectable effect: to avoid confusion with gravitational microlensing caused by stars in the lens galaxy, the background source must be larger than the optical continuum-emitting region of a QSO. We also find that substructure will cause distortions to images on milli-arcsecond scales and bias the distribution of QSO magnification ratios -- two other possible methods of detection.

Galaxy clusters contain more small-scale gravitational lenses than expected from cosmological simulations.

We demonstrate that the flux ratios of 4-image lensed quasars provide a powerful means of probing the small scale structure of Dark Matter (DM) halos. A family of smooth lens models can precisely predict certain combinations of flux ratios using only the positions of the images and lens as inputs. Using 5 observed lens systems we show that real galaxies {\it cannot} be described by smooth singular isothermal ellipsoids, nor by the more general elliptical power-law potentials. Large scale distortions from the elliptical models can not yet be ruled out. Nevertheless we find, by comparing with simulations, that the data can be accounted for by a significant ($\sim 5-10%$) amount of dark substructures within a projected distance of several kpc from the center of lenses. This interpretation favors the Cold Dark Matter (CDM) model other the warm or self-interacting DM models.Comment: 6 pages with 2 PS figs, resubmitted to ApJ Letter, added a figure to show the amount of substruture needed and tables of model parameter

Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.