arXiv subjects

H. Domínguez Sánchez

Publications and source records attributed to H. Domínguez Sánchez.

At least 19 recordsLinked to original sources

arXiv2026

Euclid: Quick Data Release (Q1) -- Exploring the detailed visual morphology of galaxies in clusters

Galaxy clusters provide unique laboratories for studying environmental effects on galaxy evolution. The morphology--density ($T$--$Σ$) and morphology--cluster-centric radius ($T$--$R$) relations trace how galaxy morphology is influenced by the environment, but previous studies at intermediate redshifts have been limited in both sample size and radial coverage. We use the Euclid Quick Data Release 1 (Q1) visual morphology catalogue to measure the $T$--$Σ$ and $T$--$R$ relations for smooth, featured-or-disc, and barred galaxies in known clusters at $0.2\leq z\leq 0.5$, extending this analysis to large cluster-centric distances ($3 R_{500c}$) and studying their dependence on stellar mass. Using photometric redshifts and stellar masses provided by Euclid, we identify 1754 cluster members within $1.5 R_{500c}$, distributed across 71 clusters. We classify the identified galaxies as smooth, featured-or-disc, or barred using the predicted vote fractions provided by the Zoobot deep learning foundation model in the Q1 visual morphology catalogue. We confirm the $T$--$Σ$ relation in all the stellar mass ranges studied, with a stronger influence of the cluster environment on galaxy morphology in the densest parts of the cluster and closer to the cluster centre. Beyond $1.5 R_{500c}$, the morphological segregation weakens, with featured-or-disc galaxies overtaking smooth galaxies at the lowest densities, consistent with the growing contribution of field interlopers in the cluster outskirts. For barred galaxies, we find a tentative decline of the bar fraction toward lower densities that is most pronounced for the most massive galaxies. In conclusion, our results show that the cluster environment drives a progressive transformation of galaxy morphology, with the loss of disc structure becoming more pronounced towards the cluster core, where environmental processes act most efficiently.

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arXiv2026

Too many quiescent late-type galaxies or a matter of misclassification?

In the local Universe, the terms late-type and early-type are used to describe, respectively, disc galaxies with spiral arms and more bulge-dominated systems lacking spiral structure. These morphologies are often associated with different star formation levels, motivating the classical scenario where late-type and early-type galaxies are seen, respectively, as star-forming and "red and dead" systems. To build a comprehensive picture of galaxy evolution, we must understand the relationship between morphology and star formation, and be able to explain more exotic configurations, such as quiescence in late-type galaxies. Large morphological catalogues are valuable tools to address these questions, but they must not be used blindly. We show that selecting quiescent galaxies classified as late-type in these catalogues leads to a clear excess of edge-on systems, with unreliable morphologies, compared with the general galaxy population in the Sloan Digital Sky Survey. Consequently, the fraction of quiescent late-type galaxies in this survey could be overestimated by a factor of two. Conversely, S0 galaxies, considered early-type discs without spiral arms, are under-represented at high inclinations. We conclude that the excess of edge-on quiescent late-type galaxies can be explained if most of these systems are misclassified edge-on S0 galaxies. We make recommendations to remove this bias.

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arXiv2026

Euclid: Quick Data Release (Q1) -- Dual AGN in low-mass galaxies

Dual active galactic nuclei (AGNs) are expected in hierarchical galaxy evolution models, in which low-mass galaxies merge to build more massive ones. While observational evidence for dual AGNs is growing in massive galaxies, no clear detection has yet been found in the low-mass regime. We used photometry and spectroscopy from the first \Euclid Quick Data Release, combined with a collection of multi-wavelength data from the Dark Energy Spectroscopic Instrument (DESI), the LOw-Frequency ARray (LOFAR) high band antenna, and counterparts in X-ray and mid-infrared catalogues to identify dual AGNs at redshift $z \lesssim 1$. Focusing on low-mass galaxies with stellar masses below 10$^{10}$ M$_{\odot}$, we find nine dual AGN candidates with projected separations ranging from $\sim$20 to 51 kpc. We also find 49 dual AGN candidates in more massive galaxies. We derive a dual AGN fraction of 0.1\% for the low-mass galaxies and estimate that these systems likely trace a population of progenitor black hole pairs that may evolve into bound binaries and eventually coalesce, emitting gravitational waves in the LISA band. These results constitute the first sample of spectroscopically confirmed dual AGN candidates in low-mass galaxies and have important implications for models in which supermassive black holes grow from lower-mass black holes located in low-mass galaxies, as well as for predictions of gravitational waves from low-mass binary black holes.

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arXiv2026

OJALÁ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars

The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation model designed to simultaneously classify astronomical objects and infer their physical parameters using 54 narrow bands from J-PAS, combined with broad bands from the DESI Legacy Imaging Surveys and WISE. The model is trained on $\sim20$ million synthetic SEDs generated from DESI DR1 spectra. We validate OJALA using a cross-matched sample of $\sim121,000$ objects between J-PAS and DESI. The model achieves a weighted F1-score of approximately 0.9 for spectral classification (stars, galaxies, and QSOs) at $i < 21$. For galaxies, we recover photo-z with a precision of $σ_{\rm NMAD} < 0.01$, while for QSOs, the precision improves significantly at $z > 1.5$, reaching $σ_{\rm NMAD} \approx 0.006$ at $z \approx 3.5$. We demonstrate robust estimation of physical properties for galaxies, recovering stellar masses and SFR with a scatter of approximately 0.11 dex and 0.22 dex, respectively. Furthermore, the model accurately predicts EWs for major optical emission lines, allowing for the derivation of extinction-corrected H$α$ luminosities with a scatter of 0.29 dex. OJALA successfully reproduces the BPT and WHAN diagnostic diagrams, classifying SF, AGN, and passive galaxies with F1-scores typically ranging from 70% to 90% depending on the diagnostic class. For stars, the model reliably infers effective temperature and metallicity, though surface gravity remains challenging. Finally, we show the modularity of the architecture by fine-tuning the pre-trained embeddings to predict BH masses, a property not included in the primary training, recovering spectroscopic virial estimates with a precision of approximately 0.5 dex. We release the code, model weights, and a comprehensive VAC for the J-PAS EDR.

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arXiv2025

Euclid preparation LXXIX. Using mock low surface brightness dwarf galaxies to probe Euclid Wide Survey detection capabilities

Local Universe dwarf galaxies can serve as both cosmological and mass assembly probes. Deep surveys have enabled the study of these objects down to the low surface brightness (LSB) regime. In this paper, we estimate Euclid's dwarf detection capabilities as well as limits of its MERge processing function (MER pipeline), which is responsible for producing the stacked mosaics and final catalogues. To do this, we injected mock dwarf galaxies in a real Euclid Wide Survey (EWS) field in the VIS band and compared the input catalogue to the final MER catalogue. The mock dwarf galaxies were generated using simple Sersic models with structural parameters (including size and surface brightness) drawn from observed dwarf galaxy catalogues. These simulations represent an idealised case in the sense they do not account for additional factors such as ellipticity, morphology, or crowding. To characterise the detected dwarfs, we used the mean surface brightness inside the effective radius SB_e (in mag arcsec^-2). The final MER catalogues achieve completenesses of 91% for SB_e in [21,24] and 54% for SB_e in [24,28]. These numbers do not take into account possible contaminants, including confusion with background galaxies at the location of the dwarfs. After taking those effects into account, they respectively became 86% and 38%. The MER pipeline performs a final local background subtraction with a small mesh size, leading to a flux loss for galaxies with R_e > 10 arcsec. By using the final MER mosaics and reinjecting this local background, we obtained an image in which we recover reliable photometric properties for objects under the arcminute scale. This background-reinjected product is thus suitable for the study of Local Universe dwarf galaxies. Euclid's data reduction pipeline serves as a test bed for other deep surveys, particularly regarding background subtraction methods, a key issue in LSB science.

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arXiv2025

J-PAS: A Neural Network Approach to Single Stellar Population Characterization

J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg$^2$ of the visible sky from Javalambre, capturing data in 56 narrow band filters. This survey promises to revolutionize galaxy evolution studies by observing $\sim$10$^8$ galaxies with low spectral resolution. A crucial aspect of this analysis involves predicting stellar population parameters from the observed galaxy photometry. In this study, we combine the exquisite J-PAS photometry with state-of-the-art single stellar population (SSP) libraries to accurately predict stellar age, metallicity, and dust attenuation with a neural network (NN) model. The NN is trained on synthetic J-PAS photometry from different SSP librares (E-MILES, Charlot & Bruzual, XSL), to enhance the robustness of our predictions against individual SSP model variations and limitations. To create mock samples with varying observed magnitudes we add artificial noise in the form of random Gaussian variations within typical observational uncertainties in each band. Our results indicate that the NN can accurately estimate stellar parameters for SSP models without evident degeneracies, surpassing a bayesian SED-fitting method on the same test set. We obtain median bias, scatter and percentage of outliers $μ$ = (0.01 dex, 0.00 dex, 0.00 mag), $σ_{NMAD}$ = (0.23 dex, 0.29 dex, 0.04 mag), f$_{o}$ = (17 %, 24 %, 1 %) at $ i \sim$17 mag for age, metallicity and dust attenuation, respectively. The accuracy of the predictions is highly dependent on the signal-to-noise (S/N) ratio of the photometry, achieving robust predictions up to $i$ $\sim$ 20 mag.

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arXiv2025

J-PLUS: Understanding outlier white dwarfs in the third data release via dimensionality reduction

We present the white dwarf catalog derived from the third data release of the Javalambre Photometric Local Universe Survey (J-PLUS DR3), which covers 3284 deg2 using 12 optical filters. A particular focus is given to the classification of outlier sources. We applied a Bayesian fitting process to the 12-band J-PLUS photometry of white dwarf candidates from Gaia EDR3. The derived parameters were effective temperature, surface gravity, and parallax. We used theoretical models from H- and He-dominated atmospheres, with priors applied to parallax and spectral type. From the posteriors, we derived the probability of an H-dominated atmosphere and of calcium absorption for each source. Outliers were identified as sources with chi2 > 23.2, indicating significant deviations from the best-fitting model. We analyzed the residuals from the fits using the UMAP technique, which enables the classification of outliers into distinct categories. The catalog includes 14844 white dwarfs with r < 20 mag and 1 < parallax < 100 mas, with 72% of the sources lacking spectroscopic (R > 500) classification. The application of UMAP identified three main types of outliers: random measurement fluctuations (391 sources), metal-polluted white dwarfs (98 sources), and two-component systems (282 sources). The last category also includes white dwarfs with strong carbon absorption lines. We validated the J-PLUS classifications by comparison with spectroscopy from SDSS and DESI, and with Gaia BP/RP spectra, confirming a one-to-one correspondence between J-PLUS photometric and spectroscopic classifications. The J-PLUS DR3 white dwarf catalog provides a robust dataset for statistical studies. The use of dimensionality reduction techniques enhances the identification of peculiar objects, making this catalog a valuable resource for the selection of interesting targets such as metal-polluted white dwarfs or binary systems.

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arXiv2025

Euclid Quick Data Release (Q1). Exploring galaxy morphology across cosmic time through Sersic fits

We present the results of the single-component Sérsic profile fitting for the magnitude-limited sample of \IE$<23$ 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 \texttt{SourceXtractor++}: one for VIS \IE images and one for a combination of three NISP images in \YE, \JE and \HE bands. We compare the resulting Sérsic parameters to other morphological measurements provided in the Q1 data release, and to the equivalent parameters based on higher-resolution \HST imaging. These comparisons confirm the consistency and the reliability of the fits to Q1 data. Our analysis of colour gradients shows that NISP profiles have systematically smaller effective radii ($R_{\rm e}$) and larger Sérsic indices ($n$) than in VIS. In addition, we highlight trends in NISP-to-VIS parameter ratios with both magnitude and $n_{\rm VIS}$. From the 2D bimodality of the $(u-r)$ colour-$\log(n)$ plane, we define a $(u-r)_{\rm lim}(n)$ that separates early- and late-type galaxies (ETGs and LTGs). We use the two subpopulations to examine the variations of $n$ across well-known scaling relations at $z<1$. ETGs display a steeper size--stellar mass relation than 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 down 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.

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arXiv2025

MaNGA AGN dwarf galaxies (MAD). III. The role of mergers and environment in active galactic nucleus activity in dwarf galaxies

Investigating whether and how galaxy mergers affect black hole growth can be determinant for black hole-galaxy evolution models and, in particular, for understanding how early Universe seed black holes grew to become supermassive. However, while mergers have been observed to enhance the active galactic nucleus (AGN) activity, and thus black hole growth in massive galaxies, it is yet not known how this relation and the role of the environment translates to dwarf galaxies (the most likely hosts of the early seed black holes), since there are scarce and mixed results in the literature. We want to assess the impact of galaxy mergers and the environment on AGN triggering in dwarf galaxies. We use a sample of 3280 dwarf galaxies with integral-field spectroscopic data from the MaNGA survey to study the AGN fraction throughout the merger process and how it is affected by the environment (characterized by galaxy isolation, being in a void, and group richness). We also compare the fraction of interacting galaxies in AGN and non-AGN dwarf galaxies. We find that dwarf galaxy mergers can ignite AGNs at separations below 20 kpc. The AGN fraction increases notoriously after the first pass and remains enhanced until the final stage. Despite this, mergers are not the dominant AGN triggering mechanism. We also find that the environment has a non-negligible impact on AGN activity in dwarf galaxies, as the AGN fraction increases when moving to lower density environments. These findings provide the most statistically robust constraints to date on the effects of dwarf galaxy mergers and environment on AGN activity and black hole growth.

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arXiv2025

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning

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 deg2. 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.

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arXiv2025

Euclid Quick Data Release (Q1). A first view of the star-forming main sequence in the Euclid Deep Fields

The star-forming main sequence (SFMS) is a tight relation observed between stellar masses and star formation rates (SFR) in a population of galaxies. This relation is observed at different redshifts, in various morphological, and environmental domains, and is key to understanding the underlying relations between a galaxy budget of cold gas and its stellar content. Euclid Quick Data Release 1 (Q1) gives us the opportunity to investigate this fundamental relation in galaxy formation and evolution. We complement the Euclid release with public IRAC observations of the Euclid Deep Fields, improving the quality of recovered photometric redshifts, stellar masses, and SFRs, as is shown both with simulations and a comparison with available spectroscopic redshifts. From Q1 data alone, we recover more than $\sim 30\,\mathrm{k}$ galaxies with $\log_{10} (M_\ast/M_\odot) > 11$, giving a precise constraint of the SFMS at the high-mass end. We investigated the SFMS, in a redshift interval between $0.2$ and $3.0$, comparing our results with the existing literature and fitting them with a parameterisation taking into account the presence of a bending of the relation at the high-mass end, depending on the bending mass, $M_0$. We find good agreement with previous results in terms of $M_0$ values, and an increasing trend for the relation scatter at higher stellar masses. We also investigate the distribution of physical (e.g. dust absorption, $A_V$, and formation age) and morphological properties (e.g., Sérsic index and radius) in the SFR--stellar mass plane, and their relation with the SFMS. These results highlight the potential of Euclid in studying the fundamental scaling relations that regulate galaxy formation and evolution in anticipation of the forthcoming Data Release 1.

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arXiv2025

MaNGA AGN dwarf galaxies (MAD) -- II. AGN outflows in dwarf galaxies

Active Galactic Nuclei (AGN) feedback is one of the most important mechanisms in galaxy evolution. It is usually found in massive galaxies and regulates star formation. Although dwarf galaxies are assumed to be regulated by supernova feedback, recent studies show evidence for the presence of AGN outflows and feedback in dwarf galaxies. We investigate the presence of AGN outflows in a sample of 2292 dwarf galaxies with AGN signatures drawn from the MaNGA survey. Thanks to the integral field unit data from MaNGA we are able to spatially resolve these outflows and study their kinematics and energetics. Using the GELATO Python code, we fit the AGN-stacked spectrum of each galaxy, which is the stack of all the spaxels classified as AGN by emission line diagnostic diagrams, and in particular the [OIII]$λ$5007Å emission line. If the galaxies show a broad [OIII] emission line component in the stacked spectrum, we run GELATO through all the spaxels that are classified as AGN in the emission line diagnostic diagrams. We find 11 new dwarf galaxies that present outflow signatures based on the presence of a broad [OIII] emission line component. Their velocity W$_{80}$ (width containing 80$\%$ of the flux of the [OIII]$λ$5007Å emission line) ranges from 205 to 566 km s$^{-1}$ and the kinetic energy rate ranges from $\sim10^{35}$ to $\sim10^{39}$ erg s$^{-1}$. Stellar processes are unlikely to explain these outflow kinetic energy rates in the case of seven dwarf galaxies. We find a correlation between the W$_{80}$ velocity and the [OIII] luminosity and between the kinetic energy rate of the outflow and the bolometric luminosity spanning from massive to dwarf galaxies. This suggests a similar behavior between the AGN outflows in the dwarf galaxy population with those in massive galaxies.

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arXiv2025

Euclid Quick Data Release (Q1): First visual morphology catalogue

We present a detailed visual morphology catalogue for Euclid's Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378000 bright ($I_E < 20.5$) or extended (area $\geq 700\,$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.

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arXiv2025

Euclid Quick Data Release (Q1), A first look at the fraction of bars in massive galaxies at $z<1$

Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fraction as a function of redshift and stellar mass is therefore important for constraining the physical processes that drive disc formation and evolution across the history of the Universe. Leveraging the unprecedented resolution and survey area of the Euclid Q1 data release combined with the Zoobot deep-learning model trained on citizen-science labels, we identify 7711 barred galaxies with $M_* \gtrsim 10^{10}M_\odot$ in a magnitude-selected sample $I_E < 20.5$ spanning $63.1 deg^2$. We measure a mean bar fraction of $0.2-0.4$, consistent with prior studies. At fixed redshift, massive galaxies exhibit higher bar fractions, while lower-mass systems show a steeper decline with redshift, suggesting earlier disc assembly in massive galaxies. Comparisons with cosmological simulations (e.g., TNG50, Auriga) reveal a broadly consistent bar fraction, but highlight overpredictions for high-mass systems, pointing to potential over-efficiency in central stellar mass build-up in simulations. These findings demonstrate Euclid's transformative potential for galaxy morphology studies and underscore the importance of refining theoretical models to better reproduce observed trends. Future work will explore finer mass bins, environmental correlations, and additional morphological indicators.

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arXiv2025

Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model

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 300 000 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.

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arXiv2025

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1

The Euclid Wide Survey (EWS) is expected to identify of order $100\,000$ galaxy-galaxy strong lenses across $14\,000$deg$^2$. The Euclid Quick Data Release (Q1) of $63.1$deg$^2$ 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 $52\pm2\%$ (grade A/B lenses) with $50\%$ completeness (for context, due to the rarity of lenses a random classifier would have a purity of $0.05\%$). 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 $\sim300$ million galaxies, and thus time investment of both experts and citizens must be carefully managed.

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arXiv2024

Automated galaxy sizes in Euclid images using the Segment Anything Model

Stellar disk truncations, also referred to as galaxy edges, are key indicators of galactic size, determined by the radial location of the gas density threshold for star formation. Accurately measuring galaxy sizes for millions of galaxies is essential for understanding the physical processes driving galaxy evolution over cosmic time. In this study, we aim to explore the potential of the Segment Anything Model (SAM), a foundation model designed for image segmentation, to automatically identify disk truncations in galaxy images. With the Euclid Wide Survey poised to deliver vast datasets, our goal is to assess SAM's capability to measure galaxy sizes in a fully automated manner. SAM was applied to a labeled dataset of 1,047 disk-like galaxies with $M_* > 10^{10} M_{\odot}$ at redshifts up to $z \sim 1$, sourced from the HST CANDELS fields. We 'euclidized' the HST galaxy images by creating composite RGB images, using the F160W (H-band), F125W (J-band), and F814W + F606W (I-band + V-band) HST filters, respectively. Using these processed images as input for SAM, we retrieved various truncation masks for each galaxy image under different configurations of the input data. We find excellent agreement between the galaxy sizes identified by SAM and those measured manually (i.e., by using the radial positions of the stellar disk edges in galaxy light profiles), with an average deviation of approximately $3\%$. This error reduces to about $1\%$ when excluding problematic cases. Our results highlight the strong potential of SAM for detecting disk truncations and measuring galaxy sizes across large datasets in an automated way. SAM performs well without requiring extensive image preprocessing, labeled training datasets for truncations (used only for validation), fine-tuning, or additional domain-specific adaptations such as transfer learning.

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arXiv2024

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

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy 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 disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 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 crowdsourcing. 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.

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