Euclid

Abstract:

The Euclid Wide Survey (EWS) is expected to identify in the order of 100 000 galaxy-galaxy strong lenses across 14 000deg 2 . The Euclid Quick Data Release (Q1) of 63.1deg 2 Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around 100 000 images, of which 65% received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of 52 ± 2% (grade A/B lenses) with 50% completeness (for context, due to the rarity of lenses a random classifier would have a purity of 0.05% and the best machine learning network in this work achieved 7.3% purity for the same completeness). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than ∼300 million galaxies, and thus the time investment of both experts and citizens must be carefully managed.

Abstract:

We present a detailed visual morphology catalogue for the Euclid Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378 000 bright ( I E < 20.5) or extended (area ≥700 pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from an intensive one-month campaign by Galaxy Zoo volunteers. Our measurements are fully automated, and hence fully scaleable. This catalogue is the first 0.4% of the approximately 100 million galaxies where Euclid will ultimately resolve detailed morphology.

Abstract:

The first Euclid Quick Data Release, Q1, comprises 63.1 deg 2 of the Euclid Deep Fields (EDFs) to the nominal wide-survey depth. It encompasses visible and near-infrared space-based imaging and spectroscopic data, ground-based photometry in the u , g , r , i , and z bands, as well as corresponding masks. Overall, Q1 contains about 30 million objects in three areas near the ecliptic poles around EDF-North and EDF-South, as well as the EDF-Fornax field in the constellation of the same name. The purpose of this data release – and its associated technical papers – is twofold. First, it is meant to inform the community of the enormous potential of the Euclid survey data, to describe what is contained in these data, and to help prepare expectations for the forthcoming first major data release DR1. Second, it enables a wide range of initial scientific projects with wide-survey Euclid data, ranging from the early Universe to the Solar System. The Q1 data were processed with early versions of the processing pipelines, which already demonstrate a good performance, with numerous improvements in implementation compared to pre-launch development. In this paper, we describe the sky areas released in Q1, the observations, a top-level view of the data processing of Euclid and associated external data, the Q1 photometric masks, and how to access the data. We also give an overview of initial scientific results obtained using the Q1 dataset by Euclid Consortium scientists, and conclude with important caveats when using the data. As a complementary product, Q1 also contains observations of a star-forming area in Lynd’s Dark Nebula 1641 in the Orion A Cloud, observed for technical purposes during Euclid ’s performance-verification phase. This is a unique target, of a type not commonly found in Euclid ’s nominal sky survey.

Abstract:

The evolution of the rest-frame ultraviolet luminosity function (UV LF) is a powerful probe of early star formation and galaxy stellar mass build-up. At z > 6, its bright end ( M UV < −21) remains poorly constrained due to small survey volumes of existing near-infrared (NIR) space-based imaging surveys. The Euclid Deep Fields (EDFs) will cover 53 deg 2 with NIR coverage down to a magnitude of 26.5 AB, providing a factor of 100 increase in area compared to previous space-based surveys. They thus offer an unprecedented opportunity to select bright z > 6 Lyman break galaxies (LBGs) and definitively constrain the bright end of the UV LF. With its NIR coverage extending to ∼2 μm, Euclid has the power to detect galaxies out to z ∼ 13. Here, we present a forecast for the number densities of z > 6 galaxies that Euclid is expected to observe in the final EDF dataset. Using synthetic photometry from spectral energy distribution (SED) templates of z = 5–15 galaxies, z = 1–4 interlopers, and Milky Way M-, L-, and T-type dwarfs, we investigate optimal selection methodologies for high- z LBGs in the EDF datasets. We find that a combination of signal-to-noise ratio (S/N) cuts with SED fitting (over optical to MIR bands) yields the highest fidelity sample, recovering more than 76% of the input synthetic z > 6 LBGs, while limiting low- z contamination to less than 10%. This contamination does not include effects from instrumental artefacts, which will impact the first Euclid data releases. Auxiliary data prove critical: optical coverage from the Hyper Suprime Camera and Vera C. Rubin Observatory will distinguish genuine Lyman breaks from contaminant features, while Spitzer Space Telescope/IRAC data are vital for recovering z > 10 sources. Based on empirical double power-law LF models, we expect more than 100 000 LBGs at z = 6–12 and more than 100 sources as far back as z > 12 in the final Euclid data release. In contrast, the steeper Schechter LF models predict no detections of z > 12 LBGs. In this work, we also present two ultra-luminous ( M UV < −23.5) candidates selected from the Q1 EDF-N dataset ( Euclid Quick Data Release). If their redshifts are reliable, their magnitudes suggest a DPL UV LF model at z > 9. This highlights the power of Euclid in constraining the bright end of the UV LF in the early Universe and in identifying the most luminous sources that are valuable for further follow-up observations.

Abstract:

Galaxy major mergers are indicated as one of the principal pathways to trigger active galactic nuclei (AGN). We present the first statistical analysis of the major merger and AGN connection in the Euclid Deep Fields, and showcase the statistical power of the Euclid data. We constructed a stellar-mass-complete ( M ★ > 10 9.8 M ⊙ ) sample of galaxies from the quick data release (Euclid Quick Release Q1 2025) in the redshift range z = 0.5–2. We selected AGN using X-ray detections, optical spectroscopy, and mid-infrared (MIR) colours, and by processing I E observations with an image decomposition algorithm. We used convolutional neural networks trained on cosmological hydrodynamic simulations to classify galaxies as mergers and non-mergers. We found a larger fraction of AGN in mergers compared to the non-merger controls for all AGN selections, with AGN excess factors ranging from two to six. The largest excess we observed was in the MIR AGN. Likewise, a generally larger merger fraction ( f merg ) was seen in active galaxies than in the non-active controls, with the excess depending on the AGN selection method. Furthermore, we analysed f merg as a function of the AGN bolometric luminosity ( L bol ) and the contribution of the point-source component to the total galaxy light in the I E -band ( f PSF ) as a proxy for the relative AGN contribution fraction. We uncovered a rising f merg , with an increasing f PSF up to f PSF ≃ 0.55, after which we observed a decreasing trend. In the range f PSF = 0.3–0.7, mergers appear to be the dominant AGN fuelling mechanism. We then derived the point-source luminosity ( L PSF ) and showed that f merg monotonically increases as a function of L PSF at z < 0.9, with f merg ≥ 50% for L PSF ≃ 2 × 10 43 erg s −1 . Similarly, at 0.9 ≤ z ≤ 2, f merg rises as a function of L PSF , though mergers do not dominate until L PSF ≃ 10 45 erg s −1 . For the X-ray and spectroscopically detected AGN, we derived the bolometric luminosity, L bol , which has a positive correlation with f merg for X-ray AGN, while there is a less pronounced trend for spectroscopically selected AGN due to the smaller sample size. At L bol > 10 45 erg s −1 , AGN mostly reside in mergers. We conclude that mergers are most strongly associated with the most powerful and dust-obscured AGN, which are typically linked to a fast-growing phase of the supermassive black hole, while other mechanisms, such as secular processes, might be the trigger of less luminous and dominant AGN.