Euclid

Abstract:

The matter around galaxy clusters is distributed over several filaments, reflecting their positions as nodes in the large-scale cosmic web. The number of filaments connected to a cluster, i.e. its connectivity, is expected to affect the physical properties of clusters. Using the first Euclid galaxy catalogue from the Euclid Quick Release 1 (Q1), we investigated the connectivity of galaxy clusters and how it correlates with their physical and galaxy member properties. Around 220 clusters located within the three fields of Q1 (covering ∼63 deg 2 ) were analysed in the redshift range 0.2 < z < 0.7. Due to the photometric redshift uncertainty, we reconstructed the cosmic web skeleton, and measured the cluster connectivity, in 2D projected slices with a thickness of 170 comoving h −1 Mpc and centred on each cluster redshift, by using two different filament finder algorithms on the most massive galaxies ( M ★ > 10 10.3 M ⊙ ). In agreement with previous measurements, we recovered the mass-connectivity relation independently of the filament detection algorithm, showing that the most massive clusters are, on average, connected to a larger number of cosmic filaments, consistent with hierarchical structure formation models. Furthermore, we explored the possible correlations between connectivities and two cluster properties: the fraction of early-type galaxies and the Sérsic index of galaxy members. Our result suggests that the clusters populated by early-type galaxies exhibit higher connectivity compared to clusters dominated by late-type galaxies. These preliminary investigations highlight our ability to quantify the impact of the cosmic web’s connectivity on cluster properties with Euclid .

Abstract:

Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal datasets that include imaging and spectroscopic information for millions of galaxies. These data serve as an ideal benchmark for large, pre-trained multi-modal models, which can leverage vast amounts of unlabelled data. In this work, we present the first exploration of Euclid data with AstroPT , an autoregressive multi-modal foundation model trained on approximately 300000 optical and infrared Euclid images and spectral energy distributions (SEDs) from the first Euclid Quick Data Release. We compare self-supervised pre-training with baseline fully supervised training across several tasks: galaxy morphology classification; redshift estimation; similarity searches; and outlier detection. Our results show that: (a) AstroPT embeddings are highly informative, correlating with morphology and effectively isolating outliers; (b) including infrared data helps to isolate stars, but degrades the identification of edge-on galaxies, which are better captured by optical images; (c) simple fine-tuning of these embeddings for photometric redshift and stellar mass estimation outperforms a fully supervised approach, even when using only 1% of the training labels; and (d) incorporating SED data into AstroPT via a straightforward multi-modal token-chaining method improves photo- z predictions, and allow us to identify potentially more interesting anomalies (such as ringed or interacting galaxies) compared to a model pre-trained solely on imaging data.

Abstract:

Active galactic nuclei (AGN) are an important phase in galaxy evolution. However, they can be difficult to identify due to their varied observational signatures. Furthermore, to understand the impact of an AGN on its host galaxy, it is important to quantify the strength of the AGN with respect to the host galaxy. We developed a deep learning (DL) model to identify AGN in imaging data by deriving the contribution of the central point source. The model was trained with Euclidised mock galaxy images in which we artificially injected different levels of AGN, in the form of varying contributions of the point spread function (PSF). Our DL-based method can precisely and accurately recover the injected AGN contribution fraction, f PSF , with a mean difference between the predicted and true f PSF of −0.0078 and an overall root mean square error of 0.051. With this new method, we move beyond the simplistic AGN versus non-AGN classification and are able to precisely quantify the AGN contribution and study galaxy evolution across a continuous spectrum of AGN activity. We applied our method to a stellar-mass-limited sample (with M * ≥ 10 9.8 M ⊙ and 0.5 ≤ z ≤ 2.0 ) selected from the first Euclid quick data release (Q1) and, using a threshold of f PSF > 0.2, we identified 61 432 ± 70 AGN over 63.1 deg 2 (9.8 ± 0.1% of our sample). We compared these DL-selected AGN with AGN selected in the X-ray, mid-infrared (MIR), and via optical spectroscopy and investigated their overlapping fractions depending on different thresholds on the PSF contribution. We find that the overlap increases with increasing X-ray or bolometric AGN luminosity. We observe a positive correlation between the luminosity in the I E filter of the AGN and the host galaxy stellar mass, suggesting that supermassive black holes (SMBHs) generally grow faster (in absolute terms, i.e. the luminosity of the PSF component is larger) in more massive galaxies. Moreover, the mean relative contribution of the AGN is higher in the quiescent galaxy population than in the star-forming population. In terms of absolute power, starburst galaxies, as well as the most massive galaxies (across the star-formation main sequence), tend to host the most luminous AGN, indicating concomitant assembly of the SMBH and the host galaxy.

Abstract:

Our understanding of cosmic star formation at z > 3 used to largely rely on rest-frame UV observations. However, these observations overlook dusty and massive sources, resulting in an incomplete census of early star-forming galaxies. Recent infrared data from Spitzer and the James Webb Space Telescope (JWST) have revealed a hidden population at z ∼ 3 − 6 with extreme red colours. Taking advantage of the overlap between imaging of the Euclid Deep Fields (EDFs), covering about 60 deg 2 , and ancillary Spitzer observations, we identified 27 000 extremely red objects with H E − IRAC2 > 2.25 (dubbed HIEROs) down to a 10 σ completeness magnitude limit of IRAC2 = 22.5 AB. After a visual investigation to discard artefacts and any objects with troubling photometry, we were left with a final sample of 3900 candidates. We retrieved the physical parameter estimates for these objects from the spectral energy distribution-fitting tool CIGALE . Our results confirm that HIERO galaxies can populate the high-mass end of the stellar mass function at z > 3, with some sources reaching extreme stellar masses ( M * > 10 11 M ⊙ ) and exhibiting high dust attenuation values ( A V > 3). However, we consider the stellar mass estimates unreliable for sources at z > 3.5. For this reason, we favour a more conservative lower- z solution. The challenges faced by spectral energy distribution-fitting tools in accurately characterising these objects underscore the need for further studies that incorporate both observations at shorter wavelengths and spectroscopic data. Euclid spectra will help resolve degeneracies and better constrain the physical properties of the brightest galaxies. Given the extreme nature of this population, characterising these sources is crucial for building a comprehensive picture of galaxy evolution and stellar mass assembly across most of the history of the Universe. This work demonstrates Euclid ’s potential to provide statistical samples of rare objects, such as massive, dust-obscured galaxies at z > 3, which will be prime targets for JWST and the Atacama Large Millimeter/submillimeter Array (ALMA).

Abstract:

This paper describes the near-infrared processing function (NIR PF) that processes NIR images from the Near-Infrared Spectrometer and Photometer (NISP) instrument on board the Euclid satellite. NIR PF consists of three main components: (i) a common pre-processing stage for both photometric (NIR) and spectroscopic (SIR) data to remove instrumental effects; (ii) astrometric and photometric calibration of NIR data, along with catalogue extraction; and (iii) resampling and stacking. The necessary calibration products are generated using dedicated pipelines that process observations from both the early performance verification (PV) phase in 2023 and the nominal survey operations. After outlining the pipelineś structure and algorithms, we describe its application to Euclid Q1 images. For Q1, we achieved an astrometric accuracy of 5–8 mas, a relative photometric accuracy of 5 mmag, along with an absolute flux calibration limited by the 1% uncertainty of the Hubble Space Telescope (HST) CALSPEC database. We characterised the point spread function (PSF), which we found to be very stable across the focal plane. Finally, we discuss current limitations of NIR PF that will be improved upon for future data releases.