Context. In metric theories of gravity with photon number conservation, the luminosity and angular diameter distances are related via the Etherington relation, also known as the distance duality relation (DDR). A violation of this relation would rule out the standard cosmological paradigm and point to the presence of new physics.Aims. We quantify the ability of Euclid, in combination with contemporary surveys, to improve the current constraints on deviations from the DDR in the redshift range 0<z<1.6.Methods. We start with an analysis of the latest available data, improving previously reported constraints by a factor of 2.5. We then present a detailed analysis of simulated Euclid and external data products, using both standard parametric methods (relying on phenomenological descriptions of possible DDR violations) and a machine learning reconstruction using genetic algorithms.Results. We find that for parametric methods Euclid can (in combination with external probes) improve current constraints by approximately a factor of six, while for non-parametric methods Euclid can improve current constraints by a factor of three.Conclusions. Our results highlight the importance of surveys like Euclid in accurately testing the pillars of the current cosmological paradigm and constraining physics beyond the standard cosmological model.

Context. In the last decade, astronomers have found a new type of supernova called superluminous supernovae (SLSNe) due to their high peak luminosity and long light-curves. These hydrogen-free explosions (SLSNe-I) can be seen to z similar to 4 and therefore, offer the possibility of probing the distant Universe. Aims. We aim to investigate the possibility of detecting SLSNe-I using ESA's Euclid satellite, scheduled for launch in 2020. In particular, we study the Euclid Deep Survey (EDS) which will provide a unique combination of area, depth and cadence over the mission. Methods. We estimated the redshift distribution of Euclid SLSNe-I using the latest information on their rates and spectral energy distribution, as well as known Euclid instrument and survey parameters, including the cadence and depth of the EDS. To estimate the uncertainties, we calculated their distribution with two different set-ups, namely optimistic and pessimistic, adopting different star formation densities and rates. We also applied a standardization method to the peak magnitudes to create a simulated Hubble diagram to explore possible cosmological constraints. Results. We show that Euclid should detect approximately 140 high-quality SLSNe-I to z similar to 3.5 over the first five years of the mission (with an additional 70 if we lower our photometric classification criteria). This sample could revolutionize the study of SLSNe-I at z > 1 and open up their use as probes of star-formation rates, galaxy populations, the interstellar and intergalactic medium. In addition, a sample of such SLSNe-I could improve constraints on a time-dependent dark energy equation-of-state, namely w (a), when combined with local SLSNe-I and the expected SN Ia sample from the Dark Energy Survey. Conclusions. We show that Euclid will observe hundreds of SLSNe-I for free. These luminous transients will be in the Euclid data-stream and we should prepare now to identify them as they offer a new probe of the high-redshift Universe for both astrophysics and cosmology.

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2-2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example z> 1. However they generally perform better than template-fitting methods at low redshift (z<0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of 0.4x2006;deg(2) as it will be seen by the Euclid visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sersic profiles. Based on these simulations, we estimate that the Euclid Wide Survey (EWS) will be able to resolve the internal morphological structure of galaxies down to a surface brightness of 22.5x2006;magx2006;arcsec(-2), and the Euclid Deep Survey (EDS) down to 24.9x2006;magx2006;arcsec(-2). This corresponds to approximately 250 million galaxies at the end of the mission and a 50% complete sample for stellar masses above 10(10.6)M(circle dot) (resp. 10(9.6)M(circle dot)) at a redshift zx2004;similar to 0.5 for the EWS (resp. EDS). The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies.

We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer Space Telescope's Infrared Array Camera (IRAC). We combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11% of the total Spitzer Space Telescope mission time. The resulting mosaics cover a total of approximately 71.5 deg(2) in the 3.6 and 4.5 mu m bands, and approximately 21.8 deg(2) in the 5.8 and 8 mu m bands. They reach at least 24 AB magnitude (measured to 5 sigma, in a 2 ''.5 aperture) in the 3.6 mu m band and up to similar to 5 mag deeper in the deepest regions. The astrometry is tied to the Gaia astrometric reference system, and the typical astrometric uncertainty for sources with 16 < [3.6] < 19 is less than or similar to 0 ''.15. The photometric calibration is in excellent agreement with previous WISE measurements. We extracted source number counts from the 3.6 mu m band mosaics, and they are in excellent agreement with previous measurements. Given that the Spitzer Space Telescope has now been decommissioned, these mosaics are likely to be the definitive reduction of these IRAC data. This survey therefore represents an essential first step in assembling multi-wavelength data on the Euclid deep fields, which are set to become some of the premier fields for extragalactic astronomy in the 2020s.

Context. The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G less than or similar to 21 mag. Aims. We showcase stellar variability in the Galactic colour-absolute magnitude diagram (CaMD). We focus on pulsating, eruptive, and cataclysmic variables, as well as on stars that exhibit variability that is due to rotation and eclipses. Methods. We describe the locations of variable star classes, variable object fractions, and typical variability amplitudes throughout the CaMD and show how variability-related changes in colour and brightness induce "motions". To do this, we use 22 months of calibrated photometric, spectro-photometric, and astrometric Gaia data of stars with a significant parallax. To ensure that a large variety of variable star classes populate the CaMD, we crossmatched Gaia sources with known variable stars. We also used the statistics and variability detection modules of the Gaia variability pipeline. Corrections for interstellar extinction are not implemented in this article. Results. Gaia enables the first investigation of Galactic variable star populations in the CaMD on a similar, if not larger, scale as was previously done in the Magellanic Clouds. Although the observed colours are not corrected for reddening, distinct regions are visible in which variable stars occur. We determine variable star fractions to within the current detection thresholds of Gaia. Finally, we report the most complete description of variability-induced motion within the CaMD to date. Conclusions. Gaia enables novel insights into variability phenomena for an unprecedented number of stars, which will benefit the understanding of stellar astrophysics. The CaMD of Galactic variable stars provides crucial information on physical origins of variability in a way that has previously only been accessible for Galactic star clusters or external galaxies. Future Gaia data releases will enable significant improvements over this preview by providing longer time series, more accurate astrometry, and additional data types (time series BP and RP spectra, RVS spectra, and radial velocities), all for much larger samples of stars.

The Outer Solar System Origins Survey (OSSOS), a wide-field imaging program in 2013-2017 with the Canada-France-Hawaii Telescope, surveyed 155 deg(2) of sky to depths of m(r) = 24.1-25.2. We present 838 outer solar system discoveries that are entirely free of ephemeris bias. This increases the inventory of trans-Neptunian objects (TNOs) with accurately known orbits by nearly 50%. Each minor planet has 20-60 Gaia/Pan-STARRS-calibrated astrometric measurements made over 2-5 oppositions, which allows accurate classification of their orbits within the trans-Neptunian dynamical populations. The populations orbiting in mean-motion resonance with Neptune are key to understanding Neptune's early migration. Our 313 resonant TNOs, including 132 plutinos, triple the available characterized sample and include new occupancy of distant resonances out to semimajor axis a similar to 130 au. OSSOS doubles the known population of the nonresonant Kuiper Belt, providing 436 TNOs in this region, all with exceptionally high-quality orbits of a uncertainty sigma(a)