Euclid: Testing photometric selection of emission-line galaxy targets

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select targets. Conversely, the Euclid NISP slitless spectrograph will record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a galaxy sample, but at the price of making the selection function harder to quantify. The Euclid Wide Survey aims at building robust statistical samples of emission-line galaxies with fluxes in the Halpha-NII complex brighter than 2e-16 erg/s/cm^2 and within 0.

Euclid: Testing photometric selection of emission-line galaxy targets

International audienceMulti-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select targets. Conversely, the Euclid NISP slitless spectrograph will record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a galaxy sample, but at the price of making the selection function harder to quantify. The Euclid Wide Survey aims at building robust statistical samples of emission-line galaxies with fluxes in the Halpha-NII complex brighter than 2e-16 erg/s/cm^2 and within 0.9<z<1.8. At faint fluxes, we expect significant contamination by wrongly measured redshifts, either due to emission-line misidentification or noise fluctuations, with the consequence of reducing the purity of the final samples. This can be significantly improved by exploiting Euclid photometric information to identify emission-line galaxies over the redshifts of interest. To this goal, we compare and quantify the performance of six machine-learning classification algorithms. We consider the case when only Euclid photometric and morphological measurements are used and when these are supplemented by ground-based photometric data. We train and test the classifiers on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. Dense neural networks and support vector classifiers obtain the best performance, with comparable results in terms of the adopted metrics. When training on Euclid photometry alone, these can remove 87% of the sources that are fainter than the nominal flux limit or lie outside the range 0.9<z<1.8, a figure that increases to 97% when ground-based photometry is included. These results show how by using the photometric information available to Euclid it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations

Euclid: Testing photometric selection of emission-line galaxy targets

International audienceMulti-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select targets. Conversely, the Euclid NISP slitless spectrograph will record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a galaxy sample, but at the price of making the selection function harder to quantify. The Euclid Wide Survey aims at building robust statistical samples of emission-line galaxies with fluxes in the Halpha-NII complex brighter than 2e-16 erg/s/cm^2 and within 0.9<z<1.8. At faint fluxes, we expect significant contamination by wrongly measured redshifts, either due to emission-line misidentification or noise fluctuations, with the consequence of reducing the purity of the final samples. This can be significantly improved by exploiting Euclid photometric information to identify emission-line galaxies over the redshifts of interest. To this goal, we compare and quantify the performance of six machine-learning classification algorithms. We consider the case when only Euclid photometric and morphological measurements are used and when these are supplemented by ground-based photometric data. We train and test the classifiers on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. Dense neural networks and support vector classifiers obtain the best performance, with comparable results in terms of the adopted metrics. When training on Euclid photometry alone, these can remove 87% of the sources that are fainter than the nominal flux limit or lie outside the range 0.9<z<1.8, a figure that increases to 97% when ground-based photometry is included. These results show how by using the photometric information available to Euclid it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations

Euclid: Testing photometric selection of emission-line galaxy targets

International audienceMulti-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select targets. Conversely, the Euclid NISP slitless spectrograph will record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a galaxy sample, but at the price of making the selection function harder to quantify. The Euclid Wide Survey aims at building robust statistical samples of emission-line galaxies with fluxes in the Halpha-NII complex brighter than 2e-16 erg/s/cm^2 and within 0.9<z<1.8. At faint fluxes, we expect significant contamination by wrongly measured redshifts, either due to emission-line misidentification or noise fluctuations, with the consequence of reducing the purity of the final samples. This can be significantly improved by exploiting Euclid photometric information to identify emission-line galaxies over the redshifts of interest. To this goal, we compare and quantify the performance of six machine-learning classification algorithms. We consider the case when only Euclid photometric and morphological measurements are used and when these are supplemented by ground-based photometric data. We train and test the classifiers on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. Dense neural networks and support vector classifiers obtain the best performance, with comparable results in terms of the adopted metrics. When training on Euclid photometry alone, these can remove 87% of the sources that are fainter than the nominal flux limit or lie outside the range 0.9<z<1.8, a figure that increases to 97% when ground-based photometry is included. These results show how by using the photometric information available to Euclid it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations

Euclid: Early Release Observations -- A preview of the Euclid era through a galaxy cluster magnifying lens

International audienceWe present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use VIS and NISP imaging to produce photometric catalogs for a total of $\sim 500\,000$ objects. The imaging data reach a $5\,\sigma$ typical depth in the range 25.1-25.4 AB in the NISP bands, and 27.1-27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we identify $30$ Lyman-break galaxy (LBG) candidates at $z>6$ and 139 extremely red sources (ERSs), most likely at lower redshift. The deeper VIS imaging compared to NISP means we can routinely identify high-redshift Lyman breaks of the order of $3$ magnitudes, which reduces contamination by brown dwarf stars and low-redshift galaxies. Spectroscopic follow-up campaigns of such bright sources will help constrain both the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at $z>6$, and constrain the physical nature of these objects. Additionally, we have performed a combined strong lensing and weak lensing analysis of A2390, and demonstrate how Euclid will contribute to better constraining the virial mass of galaxy clusters. From these data, we also identify optical and near-infrared counterparts of known $z>0.6$ clusters, which exhibit strong lensing features, establishing the ability of Euclid to characterize high-redshift clusters. Finally, we provide a glimpse of Euclid's ability to map the intracluster light out to larger radii than current facilities, enabling a better understanding of the cluster assembly history and mapping of the dark matter distribution. This initial dataset illustrates the diverse spectrum of legacy science that will be enabled by the Euclid survey

Euclid: Early Release Observations -- Programme overview and pipeline for compact- and diffuse-emission photometry

International audienceThe Euclid ERO showcase Euclid's capabilities in advance of its main mission, targeting 17 astronomical objects, from galaxy clusters, nearby galaxies, globular clusters, to star-forming regions. A total of 24 hours observing time was allocated in the early months of operation, engaging the scientific community through an early public data release. We describe the development of the ERO pipeline to create visually compelling images while simultaneously meeting the scientific demands within months of launch, leveraging a pragmatic, data-driven development strategy. The pipeline's key requirements are to preserve the image quality and to provide flux calibration and photometry for compact and extended sources. The pipeline's five pillars are: removal of instrumental signatures; astrometric calibration; photometric calibration; image stacking; and the production of science-ready catalogues for both the VIS and NISP instruments. We report a PSF with a full width at half maximum of 0.16" in the optical and 0.49" in the three NIR bands. Our VIS mean absolute flux calibration is accurate to about 1%, and 10% for NISP due to a limited calibration set; both instruments have considerable colour terms. The median depth is 25.3 and 23.2 AB mag with a SNR of 10 for galaxies, and 27.1 and 24.5 AB mag at an SNR of 5 for point sources for VIS and NISP, respectively. Euclid's ability to observe diffuse emission is exceptional due to its extended PSF nearly matching a pure diffraction halo, the best ever achieved by a wide-field, high-resolution imaging telescope. Euclid offers unparalleled capabilities for exploring the LSB Universe across all scales, also opening a new observational window in the NIR. Median surface-brightness levels of 29.9 and 28.3 AB mag per square arcsec are achieved for VIS and NISP, respectively, for detecting a 10 arcsec x 10 arcsec extended feature at the 1 sigma level

Euclid. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission

International audienceWe present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breath of Euclid's data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific analyses. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z=3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of H_E<26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission's main cosmological analysis. (abridged

Euclid. I. Overview of the Euclid mission

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 deg^2 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

Euclid. I. Overview of the Euclid mission

International audienceThe 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 deg^2 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