Keyword: euclid-consortium

2025

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  Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images

  G. Stevens, S. Fotopoulou, M.N. Bremer, T. Matamoro Zatarain, K. Jahnke, B. Margalef-Bentabol, M. Huertas-Company, M.J. Smith, M. Walmsley, M. Salvato, M. Mezcua, A. Paulino-Afonso, M. Siudek, M. Talia, F. Ricci, W. Roster, Euclid Collaboration

  Astronomy & Astrophysics

  PDF Bib Abstract

  Light emission from galaxies exhibit diverse brightness profiles, influenced by factors such as galaxy type, structural features and interactions with other galaxies. Elliptical galaxies feature more uniform light distributions, while spiral and irregular galaxies have complex, varied light profiles due to their structural heterogeneity and star-forming activity. In addition, galaxies with an active galactic nucleus (AGN) feature intense, concentrated emission from gas accretion around supermassive black holes, superimposed on regular galactic light, while quasi-stellar objects (QSO) are the extreme case of the AGN emission dominating the galaxy. The challenge of identifying AGN and QSO has been discussed many times in the literature, often requiring multi-wavelength observations. This paper introduces a novel approach to identify AGN and QSO from a single image. Diffusion models have been recently developed in the machine-learning literature to generate realistic-looking images of everyday objects. Utilising the spatial resolving power of the Euclid VIS images, we created a diffusion model trained on one million sources, without using any source pre-selection or labels. The model learns to reconstruct light distributions of normal galaxies, since the population is dominated by them. We condition the prediction of the central light distribution by masking the central few pixels of each source and reconstruct the light according to the diffusion model. We further use this prediction to identify sources that deviate from this profile by examining the reconstruction error of the few central pixels regenerated in each source's core. Our approach, solely using VIS imaging, features high completeness compared to traditional methods of AGN and QSO selection, including optical, near-infrared, mid-infrared, and X-rays.

  @misc{stevens2025EuclidInpaintingAGN, author = {{Stevens}, G. and {Fotopoulou}, S. and {Bremer}, M.~N. and {Matamoro Zatarain}, T. and {Jahnke}, K. and {Margalef-Bentabol}, B. and {Huertas-Company}, M. and {Smith}, M.~J. and {Walmsley}, M. and {Salvato}, M. and {Mezcua}, M. and {Paulino-Afonso}, A. and {Siudek}, M. and {Talia}, M. and {Ricci}, F. and {Roster}, W. and the {Euclid Collaboration}.}, title = "{Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images}", journal={Astronomy \& Astrophysics}, year={2025}, publisher={EDP sciences}, DOI="10.1051/0004-6361/202554612", }

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  Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei

  A. La Marca, L. Wang, B. Margalef-Bentabol, L. Gabarra, Y. Toba, M. Mezcua, V. Rodriguez-Gomez, F. Ricci, S. Fotopoulou, T. Matamoro Zatarain, V. Allevato, F. La Franca, F. Shankar, L. Bisigello, G. Stevens, M. Siudek, W. Roster, M. Salvato, C. Tortora, L. Spinoglio, A.W.S. Man, J.H. Knapen, M. Baes, D. O'Ryan, Euclid Collaboration

  Astronomy & Astrophysics

  PDF Bib Abstract

  Galaxy major mergers are indicated as one of the principal pathways to trigger active galactic nuclei (AGN). We present the first detection of major mergers in the Euclid Deep Fields and analyse their connection with AGN, showcasing the statistical power of the Euclid data. We constructed a stellar-mass-complete (M⋆>109.8 M⊙) sample of galaxies from the first quick data release (Q1), in the redshift range z=0.5-2. We selected AGN using X-ray detections, optical spectroscopy, mid-infrared (MIR) colours, and processing IE 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 2 to 6. The largest excess is seen in the MIR-selected AGN. Likewise, a generally larger merger fraction (fmerg) is seen in active galaxies than in the non-active controls, with the excess depending on the AGN selection method. Furthermore, we analysed fmerg as a function of the AGN bolometric luminosity (Lbol) and the contribution of the point-source component to the total galaxy light in the IE-band (fPSF) as a proxy for the relative AGN contribution fraction. We uncovered a rising fmerg, with increasing fPSF up to fPSF≃0.55, after which we observed a decreasing trend. In the range fPSF=0.3-0.7, mergers appear to be the dominant AGN fuelling mechanism. We then derived the point-source luminosity (LPSF) and showed that fmerg monotonically increases as a function of LPSF at z<0.9, with fmerg≥50% for LPSF≃2x1043 erg/s. Similarly, at 0.9≤z≤2, fmerg rises as a function of LPSF, though mergers do not dominate until LPSF≃1045 erg/s. For the X-ray and spectroscopically detected AGN we derived the bolometric luminosity, Lbol, which has a positive correlation with fmerg for X-ray AGN, while there is a less pronounced trend for spectroscopically-selected AGN due to the smaller sample size. At Lbol>1045 erg/s, AGN mostly reside in mergers. We concluded that mergers are most strongly associated with the most powerful and dust-obscured AGN, 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.

  @article{2025LaMarcaMergers, title = {Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei}, author = {{La Marca}, A. and {Wang}, L. and {Margalef-Bentabol}, B. and {Gabarra}, L. and {Toba}, Y. and {Mezcua}, M. and {Rodriguez-Gomez}, V. and {Ricci}, F. and {Fotopoulou}, S. and {Matamoro Zatarain}, T. and {Allevato}, V. and {La Franca}, F. and {Shankar}, F. and {Bisigello}, L. and G. {Stevens} G. and {Siudek}, M. and {Roster}, W. and {Salvato}, M. and {Tortora}, C. and {Spinoglio}, L. and {Man}, A.W.S. and {Knapen}, J.H. and {Baes}, M. and {O\'Ryan}, D. and the {Euclid Collaboration}.}, journal={Astronomy \& Astrophysics}, year={2025}, publisher={EDP sciences}, DOI="10.1051/0004-6361/202554579", }
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  Euclid Quick Data Release (Q1). The active galaxies of Euclid

  T. Matamoro Zatarain, S. Fotopoulou, F. Ricci, M. Bolzonella, F. La Franca, A. Viitanen, G. Zamorani, M. B. Taylor, M. Mezcua, B. Laloux, A. Bongiorno, K. Jahnke, G. Stevens, R. A. Shaw, L. Bisigello, W. Roster, Y. Fu, B. Margalef-Bentabol, A. La Marca, F. Tarsitano, A. Feltre, J. Calhau, X. Lopez Lopez, M. Scialpi, M. Salvato, V. Allevato, M. Siudek, C. Saulder, D. Vergani, M. N. Bremer, L. Wang, M. Giulietti, D.M. Alexander, D. Sluse, F. Shankar, L. Spinoglio, D. Scott, R. Shirley, H. Landt, M. Selwood, Y. Toba, P. Dayal, Euclid Collaboration

  Astronomy & Astrophysics

  PDF Bib Abstract

  We present a catalogue of candidate active galactic nuclei (AGN) in the Euclid Quick Release (Q1) fields. For each Euclid source we collect multi-wavelength photometry and spectroscopy information from Galaxy Evolution Explorer (GALEX), Gaia, Dark Energy Survey (DES), Wise-field Infrared Survey Explorer (WISE), Spitzer, Dark Energy Survey (DESI), and Sloan Digital Sky Survey (SDSS), including spectroscopic redshift from public compilations. We investigate the AGN contents of the Q1 fields by applying selection criteria using Euclid colours and WISE-AllWISE cuts finding respectively 292,222 and 65,131 candidates. We also create a high-purity QSO catalogue based on Gaia DR3 information containing 1971 candidates. Furthermore, we utilise the collected spectroscopic information from DESI to perform broad-line and narrow-line AGN selections, leading to a total of 4392 AGN candidates in the Q1 field. We investigate and refine the Q1 probabilistic random forest QSO population, selecting a total of 180,666 candidates. Additionally, we perform SED fitting on a subset of sources with available zspec, and by utilizing the derived AGN fraction, we identify a total of 7766 AGN candidates. We discuss purity and completeness of the selections and define two new colour selection criteria (JH_IEY and IEH_gz) to improve on purity, finding 313,714 and 267,513 candidates respectively in the Q1 data. We find a total of 229,779 AGN candidates equivalent to an AGN surface density of 3641 deg^-2 for 18<IE≤24.5, and a subsample of 30,422 candidates corresponding to an AGN surface density of 482 deg^-2 when limiting the depth to 18<IE≤22. The surface density of AGN recovered from this work is in line with predictions based on the AGN X-ray luminosity functions.

  @article{2025MatamoroZatarainActive, title = {Euclid Quick Data Release (Q1). The active galaxies of Euclid}, author = {{Matamoro Zatarain}, T. and {Fotopoulou}, S. and {Ricci}, F. and {Bolzonella}, M. and {La Franca}, F. and {Viitanen}, A. and {Zamorani}, G. and {Taylor}, M.B. and {Mezcua}, M. and {Laloux}, B. and {Bongiorno}, A. and {Jahnke}, K. and {Stevens}, G. and {Shaw}, R.~A. and {Bisigello}, L. and {Roster}, W. and {Fu}, Y. and {Margalef-Bentabol}, B. and {La Marca}, A. and {Tarsitano}, F. and {Feltre}, A. and {Calhau}, J. and {Lopez Lopez}, X. and {Scialpi}, M. and {Salvato}, M. and {Allevato}, V. and {Siudek}, M. and {Saulder}, C. and {Vergani}, D. and {Bremer}, M.~N. and {Wang}, L. and {Giulietti}, M. and {Alexander}, D.~M. and {Sluse}, D. and {Shankar}, F. and {Spinoglio}, L. and {Scott}, D. and {Shirley}, R. and {Landt}, H. and {Selwood}, M. and {Toba}, Y. and {Dayal}, P. and the {Euclid Collaboration}}, journal={Astronomy \& Astrophysics}, year={2025}, publisher={EDP sciences}, DOI= "10.1051/0004-6361/202554619", }
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  Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model

  M. Siudek, M. Huertas-Company, M. Smith, G. Martinez-Solaeche, F. Lanusse, S. Ho, E. Angeloudi, P. A. C. Cunha, H. Domínguez Sánchez, M. Dunn, Y. Fu, P. Iglesias-Navarro, J. Junais, J. H. Knapen, B. Laloux, M. Mezcua, W. Roster, G. Stevens, J. Vega-Ferrero, Euclid Collaboration

  Astronomy & Astrophysics

  PDF Bib Abstract

  Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal data sets 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.

  @article{Siudek2025EuclidFoundation, author = {{Siudek}, M. and {Huertas-Company}, M. and {Smith}, M. and {Martinez-Solaeche}, G. and {Lanusse}, F. and {Ho}, S. and {Angeloudi}, E. and {Cunha}, P.~A.~C. and {Domínguez Sánchez}, H. and {Dunn}, M. and {Fu}, Y. and {Iglesias-Navarro}, P. and {Junais}, J. and {Knapen}, J.~H. and {Laloux}, B. and {Mezcua}, M. and {Roster}, W. and {Stevens}, G. and {Vega-Ferrero}, J. and the {Euclid Collaboration}.}, title = "{Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model}", journal={Astronomy \& Astrophysics}, year={2025}, publisher={EDP sciences}, DOI= "10.1051/0004-6361/202554611", }

2024

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  Euclid preparation - XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

  B. Aussel, S. Kruk, M. Walmsley, M. Huertas-Company, M. Castellano, C.J. Conselice, M. Delli Veneri, H. Domínguez-Sánchez, P.-A. Duc, U. Kuchner, A. La Marca, B. Margalef-Bentabol, F.R. Marleau, G. Stevens, Y. Toba, C. Tortora, L. Wang, Euclid Collaboration

  Astronomy & Astrophysics

  PDF Bib Abstract

  The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.

  @article{ Aussel2024euclid, title={Euclid preparation-XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning}, author={{Aussel}, B. and {Kruk}, S. and {Walmsley}, M. and {Castellano}, M. and {Conselice}, C.J. and {Delli Veneri}, M. and {Dominguez Sanchez}, H. and {Duc}, P.-A. and {Knapen}, J.H. and {Kuchner}, U. and {La Marca}, A. and {Margalef-Bentabol}, B. and {Marleau}, F.R. and {Stevens}, G. and {Toba}, Y. and {Tortora}, C. and {Wang}, L. and the {Euclid Collaboration}}, journal={Astronomy \& Astrophysics}, volume={689}, pages={A274}, year={2024}, publisher={EDP sciences}, DOI= "10.1051/0004-6361/202449609", }