Beecroft Institute for Particle Astrophysics and Cosmology

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.

Abstract:

To better understand the role of active galactic nuclei (AGN) in galaxy evolution, it is crucial to work with a complete and pure AGN sample. X-ray surveys are key to doing so, but their larger positional uncertainties complicate counterpart (CTP) association, further compounded by the limited availability of deep, uniform multi-wavelength ancillary data. Euclid is revolutionising this identification effort, offering extensive coverage of nearly the entire extragalactic sky, particularly in the near-infrared bands, where AGN are more easily detected. Using the first Euclid Quick Data Release (Q1), we validated the methods for identifying and classifying Euclid CTPs of known point-like sources from major X-ray surveys, including XMM- Newton , Chandra , and eROSITA. Using Bayesian statistics, combined with machine learning (ML), as incorporated in the algorithm NWAY , we identified the CTPs of 11 286 X-ray sources from the three X-ray telescopes. For the large majority of 10 194 sources, the association is unique, with the remaining ∼10% of multi-CTP cases equally split between XMM- Newton and eROSITA. Six percent of the Euclid CTPs are detected in more than one X-ray survey. We then used ML to distinguish between Galactic (8%) and extragalactic (92%) sources. We computed photo- z s using deep learning for the 9259 sources detected in the tenth data release of the DESI Legacy Survey, reaching an accuracy and a fraction of outliers of roughly 5%. Based on their X-ray luminosities, all CTPs identified as extragalactic are classified as AGN, most of which appear as type I AGN according to their hardness ratios. With this paper, we release our catalogue, which includes identifiers, basic X-ray properties, the reliability of the associations, and additional property extensions, such as Galactic- or extragalactic classifications and photometric/spectroscopic redshifts. We also provide probabilities for sub-selecting the sample based on purity and completeness, in order to allow users to tailor the sample according to their specific needs.

Abstract:

Red quasars constitute an important but elusive phase in the evolution of supermassive black holes, where dust obscuration can significantly alter their observed properties. They have broad emission lines, like other quasars, but their optical continuum emission is significantly reddened, which is why they were traditionally identified based on near- and mid-infrared selection criteria. This work showcases the capability of the Euclid space telescope to find a large sample of red quasars, using Euclid near infrared (NIR) photometry. We first conduct a forecast analysis, comparing a synthetic catalogue of red quasars with COSMOS2020. Using template fitting, we reconstruct Euclid -like photometry for the COSMOS sources and identify a sample of candidates in a multi-dimensional colour-colour space achieving 98% completeness for mock red quasars with 30% contaminants. To refine our selection function, we implement a probabilistic Random Forest classifier, and use UMAP visualisation to disentangle non-linear features in colour-space, reaching 98% completeness and 88% purity. A preliminary analysis of the candidates in the Euclid Deep Field Fornax (EDF-F) shows that, compared to VISTA+DECam-based colour selection criteria, Euclid ’s superior depth, resolution, and optical-to-NIR coverage improves the identification of the reddest, most obscured sources. Notably, the Euclid exquisite resolution in the I E filter unveils the presence of a candidate dual quasar system, highlighting the potential for this mission to contribute to future studies on the population of dual AGN. The resulting catalogue of candidates, including more the 150 000 sources, provides a first census of red quasars in Euclid Q1 and sets the groundwork for future studies in the Euclid Wide Survey (EWS), including spectral follow-up analyses and host morphology characterisation.

Abstract:

Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0 . ″ 16 resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with 170 000 lenses forecasted to be discovered amongst the 1.5 billion galaxies it will observe. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of Euclid data (Q1) covering 63 deg 2 . The models have been validated by citizen scientists and expert visual inspection. We focus on the best-performing network: a fine-tuned version of the Zoobot pretrained model originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that Zoobot was tasked to find strong lenses within, the top 1000 ranked objects contain 122 grade A lenses (almost-certain lenses) and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection yielded 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top 20 000 by Zoobot . When extrapolated to the full Euclid survey, the highest ranked one million images will contain 75 000 grade A or B strong gravitational lenses.