Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Using the vast statistics provided by both Euclid and the LOFAR surveys, we present the first large-scale study of the connection among radio emission, its morphology, and the merging properties of the hosts of radio sources up to z ∼ 2. By dividing the radio sample into active galactic nuclei (AGNs) and star-forming galaxies, we find that radio-emitting AGNs show a clear preference for residing within galaxies undergoing a merging event. This is more significant for AGNs that present complex or extended radio emission; indeed, about half of them are associated with merging systems, while only ∼15% are hosted by a non-interacting galaxy. The observed trend is primarily driven by AGNs residing at z < 1, especially in the case of high – P144 MHz > 10²⁴ W Hz⁻¹ sr⁻¹ – radio luminosities (∼56% in mergers versus ∼10% in non-mergers regardless of radio appearance). On the other hand, this preference seems to disappear at higher redshifts, where only bright AGNs with extended radio emission still prefer galaxies undergoing a merging event. The situation is reversed in the case of radio-emitting star-forming galaxies, which are preferentially associated with non-interacting systems. This is more significant as we move towards low-radio-luminosity/star-formation objects (P144 MHz < 10²³ W Hz⁻¹ sr⁻¹) for which we find ∼40% in non-mergers versus ∼20% in mergers. These values hold regardless of redshift. We interpreted the above result for AGNs with their need to accrete outer gas from local encounters in order to trigger (radio) activity, especially in the case of extended radio emission such as hot spots and lobes. This is mostly observed at z < 1, since galaxies in the local Universe are more gas deprived than their higher redshift counterparts. Internal gas reservoirs instead seem sufficient to trigger star formation within the majority of galaxies, which indeed mostly appear as non-interacting systems at all redshifts probed.

Abstract:

This paper describes the VIS processing function (VIS PF) of the Euclid ground segment pipeline, which processes and calibrates raw data from the VIS camera. We present the algorithms used in each processing element along with a description of the on-orbit performance of VIS PF based on performance verification and Q1 datasets. We demonstrate that the principal performance metrics (image quality, astrometric accuracy, photometric calibration) are within pre-launch specifications. The image-to-image photometric scatter is less than 0.8% and absolute astrometric accuracy compared to Gaia is 5 mas. Image quality is stable over all Q1 images, with a full width at half maximum (FWHM) of 0 . ″ 16. The stacked images (combining four nominal and two short exposures) reach I E = 25.6 (10 σ , measured as the variance of 1 . ″ 3 diameter apertures). We also describe quality control metrics provided with each image, and an appendix provides a detailed description of the provided data products. The excellent quality of these images demonstrates the immense potential of Euclid VIS data for weak lensing. VIS data covering most of the extragalactic sky will provide a lasting high-resolution atlas of the Universe.

Abstract:

We present the results of the single-component Sérsic profile fitting for the magnitude-limited sample of I E < 23 galaxies within the 63.1 deg 2 area of the Euclid Quick Data Release (Q1). The associated morphological catalogue includes two sets of structural parameters fitted using SourceXtractor++ : one for VIS I E images and one for a combination of three NISP images in Y E , J E , and H E bands. We compared the resulting Sérsic parameters to other morphological measurements provided in the Q1 data release and to the equivalent parameters based on higher-resolution Hubble Space Telescope imaging. These comparisons confirmed the consistency and the reliability of the fits to Q1 data. Our analysis of colour gradients shows that NISP profiles systematically have smaller effective radii ( R e ) and larger Sérsic indices ( n ) than in VIS. In addition, we highlight trends in NISP-to-VIS parameter ratios with both magnitude and n VIS . From the 2D bimodality of the ( u − r ) colour-log( n ) plane, we defined a ( u − r ) lim ( n ) that separates early- and late-type galaxies (ETGs and LTGs). We used the two sub-populations to examine the variations of n across well-known scaling relations at z < 1. The ETGs display a steeper size–stellar mass relation than the LTGs, indicating a difference in the main drivers of their mass assembly. Similarly, LTGs and ETGs occupy different parts of the stellar mass–star-formation rate plane, with ETGs at higher masses than LTGs and further below the main sequence of star-forming galaxies. This clear separation highlights the link known between the shutdown of star formation and morphological transformations in the Euclid imaging data set. In conclusion, our analysis demonstrates both the robustness of the Sérsic fits available in the Q1 morphological catalogue and the wealth of information they provide for studies of galaxy evolution with 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.