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M. Siudek

Publications and source records attributed to M. Siudek.

At least 55 records · Page 3Linked to original sources

Fueling, Evolution, and Diversity of AGN in Dwarf Galaxies: Insights from Star Formation and Black Hole Scaling Relations

We investigate the star formation activity and black hole scaling relations in a sample of 1451 AGN hosted by dwarf galaxies at redshift 0.5 to 0.9, drawn from the VIPERS survey. The sample comprises Seyferts and LINERs identified through emission-line diagnostics, as well as IR-selected AGN based on WISE colors. Using the parameter SFRnorm, defined as the ratio of the SFR of a galaxy hosting an AGN to the median SFR of star-forming galaxies of similar stellar mass and redshift, we compare AGN hosts to a control sample of non-AGN star-forming galaxies. We examine how SFRnorm varies with AGN power ([O III] luminosity), black hole mass, local environment, and stellar population age. We also analyze the MBH-Mstar relation and the evolution of the MBH/Mstar ratio, incorporating comparisons to X-ray AGN and high-redshift quasars (z > 4). Our key findings are: (i) all AGN populations show suppressed star formation at low AGN luminosities, with SFRnorm rising above unity at different luminosity thresholds depending on AGN type; (ii) LINERs show flat SFRnorm trends with MBH, remaining broadly consistent with unity; Seyferts display a mild increase with MBH, while IR AGN show a more pronounced positive trend; (iii) LINERs exhibit older stellar populations than Seyferts; (iv) at fixed stellar mass, Seyferts host more massive black holes than LINERs, with IR AGN falling in between; (v) the MBH/Mstar ratio is elevated relative to local scaling relations and remains approximately constant with redshift, in agreement with high-z AGN; (vi) the ratio decreases with stellar mass up to log(Mstar/Msun) approximately 11, beyond which it flattens toward values consistent with those of local, inactive galaxies, with this trend clearest for Seyferts and IR AGN. These results suggest that AGN in dwarf galaxies follow diverse evolutionary pathways shaped by gas availability, feedback, and selection effects.

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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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DESI DR1 Ly$α$ 1D power spectrum: The optimal estimator measurement

The one-dimensional power spectrum $P_{\mathrm{1D}}$ of Ly$α$ forest offers rich insights into cosmological and astrophysical parameters, including constraints on the sum of neutrino masses, warm dark matter models, and the thermal state of the intergalactic medium. We present the measurement of $P_{\mathrm{1D}}$ using the optimal quadratic maximum likelihood estimator applied to over 300,000 Ly$α$ quasars from Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) survey. This sample represents the largest to date for $P_{\mathrm{1D}}$ measurements and is larger than the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) by a factor of 1.7. We conduct a meticulous investigation of instrumental and analysis systematics and quantify their impact on $P_{\mathrm{1D}}$. This includes the development of a cross-exposure estimator that eliminates the need to model the pipeline noise and has strong potential for future $P_{\mathrm{1D}}$ measurements. We also present new insights into metal contamination through the 1D correlation function. Using a fitting function we measure the evolution of the Ly$α$ forest bias with high precision: $b_F(z) = (-0.218\pm0.002)\times((1 + z) / 4)^{2.96\pm0.06}$. In a companion validation paper, we substantially extend our previous suite of CCD image simulations to quantify the pipeline's exquisite performance accurately. In another companion paper, we present DR1 $P_{\mathrm{1D}}$ measurements using the Fast Fourier Transform (FFT) approach to power spectrum estimation. These two measurements produce a forest bias parameter that differs by 2.2 sigma. However, our model is simplistic, so this disagreement will be investigated in future work.

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Euclid: The potential of slitless infrared spectroscopy: A z=5.4 quasar and new ultracool dwarfs

We demonstrate the potential of Euclid's slitless spectroscopy to discover high-redshift (z>5) quasars and their main photometric contaminant, ultracool dwarfs. Sensitive infrared spectroscopy from space is able to efficiently identify both populations, as demonstrated by Euclid Near-Infrared Spectrometer and Photometer Red Grism (NISP RGE) spectra of the newly discovered z=5.404 quasar EUCL J181530.01+652054.0, as well as several ultracool dwarfs in the Euclid Deep Field North and the Euclid Early Release Observation field Abell 2764. The ultracool dwarfs were identified by cross-correlating their spectra with templates. The quasar was identified by its strong and broad CIII] and MgII emission lines in the NISP RGE 1206-1892 nm spectrum, and confirmed through optical spectroscopy from the Large Binocular Telescope. The NISP Blue Grism (NISP BGE) 926-1366 nm spectrum confirms CIV and CIII] emission. NISP RGE can find bright quasars at z~5.5 and z>7, redshift ranges that are challenging for photometric selection due to contamination from ultracool dwarfs. EUCL J181530.01+652054.0 is a high-excitation, broad absorption line quasar detected at 144 MHz by the LOw-Frequency Array (L144=4e25 W/Hz). The quasar has a bolometric luminosity of 3e12 Lsun and is powered by a 3.4e9 Msun black hole. The discovery of this bright quasar is noteworthy as fewer than one such object was expected in the ~20 deg2 surveyed. This finding highlights the potential and effectiveness of NISP spectroscopy in identifying rare, luminous high-redshift quasars, previewing the census of these sources that Euclid's slitless spectroscopy will deliver over about 14,000 deg2 of the sky.

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Deep Extragalactic VIsible Legacy Survey (DEVILS): Evolution of the Morphology-Density Relation

Galaxies with different morphological characteristics likely have different evolutionary histories, such that understanding the mechanisms that drive morphological change can provide valuable insights into the galaxy evolution process. These mechanisms largely correlate with local environment, ultimately leading to the well-known local morphology-density relation. To explore how the morphology-density relation is produced, we must look to earlier times, and trace the co-evolution of environment and morphology in an un-biased and self-consistent manner. Here we use new environmental metrics from the Deep Extragalactic VIsible Legacy Survey (DEVILS) to explore the spectroscopic morphology-density relation at intermediate redshift (0.3<z<0.5) and compare directly to the Galaxy And Mass Assembly Survey (GAMA) at 0<z<0.08. Importantly, both the galaxy morphologies and environmental metrics in DEVILS and GAMA are derived in a very similar manner, reducing any methodology biases. We see a clear evolution in morphological classes between DEVILS and GAMA, which is modulated by environment. These trends are consistent with a scenario where in all environments disk-dominated galaxies are transitioning to classical bulge+disk systems (potentially via minor mergers and/or secular evolution), and in high-density environments there is an increasing prevalence of visually-selected elliptical galaxies (potentially via major mergers and/or disk fading); with the fraction of ellipticals increasing by ~0.3 in the most dense regions over the last ~7Gyr, but remaining largely unchanged in low-density environments.

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Euclid preparation. Spatially resolved stellar populations of local galaxies with Euclid: a proof of concept using synthetic images with the TNG50 simulation

The European Space Agency's Euclid mission will observe approximately 14,000 $\rm{deg}^{2}$ of the extragalactic sky and deliver high-quality imaging for many galaxies. The depth and high spatial resolution of the data will enable a detailed analysis of stellar population properties of local galaxies. In this study, we test our pipeline for spatially resolved SED fitting using synthetic images of Euclid, LSST, and GALEX generated from the TNG50 simulation. We apply our pipeline to 25 local simulated galaxies to recover their resolved stellar population properties. We produce 3 types of data cubes: GALEX + LSST + Euclid, LSST + Euclid, and Euclid-only. We perform the SED fitting tests with two SPS models in a Bayesian framework. Because the age, metallicity, and dust attenuation estimates are biased when applying only classical formulations of flat priors, we examine the effects of additional priors in the forms of mass-age-$Z$ relations, constructed using a combination of empirical and simulated data. Stellar-mass surface densities can be recovered well using any of the 3 data cubes, regardless of the SPS model and prior variations. The new priors then significantly improve the measurements of mass-weighted age and $Z$ compared to results obtained without priors, but they may play an excessive role compared to the data in determining the outcome when no UV data is available. The spatially resolved SED fitting method is powerful for mapping the stellar populations of galaxies with the current abundance of high-quality imaging data. Our study re-emphasizes the gain added by including multiwavelength data from ancillary surveys and the roles of priors in Bayesian SED fitting. With the Euclid data alone, we will be able to generate complete and deep stellar mass maps of galaxies in the local Universe, thus exploiting the telescope's wide field, NIR sensitivity, and high spatial resolution.

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Deep Extragalactic VIsible Legacy Survey (DEVILS): Satellite Quenching at Intermediate Redshift

Determining the processes by which galaxies transition from a star-forming to a quiescent state (quenching) is paramount to our understanding of galaxy evolution. One of the key mechanisms by which this takes place is via a galaxy's interactions with a local, over-dense environment (satellite or environmental quenching). In the very local Universe, we see these processes in action, and can also observe their effects via the distribution of satellite galaxy properties. However, extending similar analyses outside of the local Universe is problematic, largely due to the difficulties in robustly defining environments with small and/or incomplete spectroscopic samples. We use new environmental metrics from the high-completeness Deep Extragalactic VIsible Legacy Survey (DEVILS) to explore the properties of satellite galaxies at intermediate redshift (0.3$<$z$<$0.5) and compare directly to the Galaxy And Mass Assembly Survey (GAMA) at 0$<$z$<$0.2. Importantly, both the galaxy properties and environmental metrics in DEVILS and GAMA are derived in an identical manner, reducing any methodology biases. We find: i) that satellite galaxies in DEVILS and GAMA show suppressed star-formation in comparison to isolated systems at the same stellar mass, by $\sim$0.5dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$), ii) that this suppression is strongest in higher mass dark matter halos (up to $\sim$1dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$) in the most massive halos) and iii) that at fixed stellar and halo mass, this suppression increases with time - with satellite passive fractions increasing by $\sim$10-15\% over the last $\sim$5Gyr. This is consistent with previous observations and numerical simulations.

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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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Beyond Traditional Diagnostics: Identifying Active Galactic Nuclei with Spectral Energy Distribution Fitting in DESI Data

Active galactic nuclei (AGN) are typically identified through their distinctive X-ray or radio emissions, mid-infrared (MIR) colors, or emission lines. However, each method captures different subsets of AGN due to signal-to-noise (SNR) limitations, redshift coverage, and extinction effects, underscoring the necessity for a multi-wavelength approach for comprehensive AGN samples. This study explores the effectiveness of spectral energy distribution (SED) fitting as a robust method for AGN identification. Using {\tt CIGALE} optical-MIR SED fits on DESI Early Data Release galaxies, we compare SED-based AGN selection ({\tt AGNFRAC} $\geq0.1$) with traditional methods including BPT diagrams, WISE colors, X-ray, and radio diagnostics. SED fitting identifies $\sim 70\%$ of narrow/broad-line AGN and 87\% of WISE-selected AGN. Incorporating high SNR WISE photometry reduces star-forming galaxy contamination from 62\% to 15\%. Initially, $\sim50\%$ of SED-AGN candidates are undetected by standard methods, but additional diagnostics classify $\sim85\%$ of these sources, revealing LINERs and retired galaxies potentially representing evolved systems with weak AGN activity. Further spectroscopic and multi-wavelength analysis will be essential to determine the true AGN nature of these sources. SED fitting provides complementary AGN identification, unifying multi-wavelength AGN selections. This approach enables more complete -- albeit with some contamination -- AGN samples essential for upcoming large-scale surveys where spectroscopic diagnostics may be limited.

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Deep Extragalactic VIsible Legacy Survey (DEVILS): The sSFR-M$_{\star}$plane part I: The recent SFH of galaxies and movement through the plane

In a recent paper we parameterised the evolution of the star-formation rate dispersion ($σ_{SFR}$) across the specific star-formation rate - stellar mass plane (sSFR-M$_{\star}$) using the Deep Extragalactic VIsible Legacy Survey (DEVILS) - suggesting that the point at which the minimum in the dispersion occurs (M$^{*}_{σ-min}$) defines a boundary between different physical mechanisms affecting galaxy evolution. Here we expand upon that work to determine the movement of galaxies through the sSFR-M$_{\star}$ plane using their recent star-formation histories (SFHs) and explore how this leads to the observed $σ_{SFR}$-M$_{\star}$ relation. We find that galaxies in sub-regions of the sSFR-M$_{\star}$ plane show distinctly different SFHs, leading to a complex evolution of the sSFR-M$_{\star}$ plane and star-forming sequence (SFS). However, we find that selecting galaxies based on stellar mass and position relative to SFS alone (as is traditionally the case), may not identify sources with common recent SFHs, and therefore propose a new selection methodology. We then use the recent SFH of galaxies to measure the evolution of the SFS, showing that it has varying contributions from galaxies with different SFHs that lead to the observed changes in slope, normalisation and turnover stellar mass. Finally, we determine the overall evolution of the sSFR-M$_{\star}$ plane from $z\sim1$ to today. In the second paper in this series we will discuss physical properties of galaxies with common recent SFHs and how these lead to the observed $σ_{SFR}$-M$_{\star}$ relation and evolution of the sSFR-M$_{\star}$ plane.

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Deep Extragalactic VIsible Legacy Survey (DEVILS): The sSFR-M$_{\star}$ plane part II: Starbursts, SFHs and AGN Feedback

In part I of this series we discussed the variation of star-formation histories (SFHs) across the specific star formation rate - stellar mass plane (sSFR-M$_{\star}$) using the Deep Extragalactic VIsible Legacy Survey (DEVILS). Here we explore the physical mechanisms that are likely driving these observational trends, by comparing the properties of galaxies with common recent SFH shapes. Overall, we find that the processes shaping the movement of galaxies through the sSFR-M$_{\star}$ plane can be be largely split into two stellar mass regimes, bounded by the minimum SFR dispersion ($σ_{SFR}$) point. At lower stellar masses we find that large $σ_{SFR}$ values are likely observed due to a combination of stochastic star-formation processes and a large variety in absolute sSFR values, but relatively constant/flat SFHs. While at higher stellar masses we see strong observational evidence that Active Galactic Nuclei (AGN) are associated with rapidly declining SFHs, and that these galaxies reside in the high $σ_{SFR}$ region of the plane. As such, we suggest that AGN feedback, leading to galaxy quenching, is the primary driver of the high $σ_{SFR}$ values. These results are consistent with previous theoretical interpretations of the $σ_{SFR}$-M$_{\star}$ relation.

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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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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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Euclid preparation. LXVIII. Extracting physical parameters from galaxies with machine learning

The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. We investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity and age. We generate noise-free, synthetic high-resolution imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images are generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We use a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a $\leq 0.130 {\rm \,dex}$ scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but still contain significant information which originates from underlying correlations at a sub-kpc scale between stellar mass surface density and stellar population properties.

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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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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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Euclid Quick Data Release (Q1). First Euclid statistical study of the active galactic nuclei contribution fraction

Active galactic nuclei (AGN) play a key role in galaxy evolution but are challenging to identify due to their varied observational signatures. Furthermore, understanding their impact requires quantifying their strength relative to their host galaxies. We developed a deep learning (DL) model for identifying AGN in imaging data by deriving the contribution of the central point source. Trained on Euclidised mock galaxy images with injected AGN levels, in the form of varying contributions of the point-spread function (PSF), our model can precisely and accurately recover the injected AGN contribution fraction $f_{\rm PSF}$, with a mean difference between the predicted and true $f_{\rm PSF}$ of $-0.0078$ and an overall root mean square error (RMSE) of 0.051. This method moves beyond binary AGN classification, enabling precise AGN contribution measurements. Applying our model to a stellar-mass-limited sample ($M_{\ast} \ge 10^{9.8} M_{\odot}$, $0.5 \le z \le 2.0$) from the first \Euclid quick data release (Q1), we identify $48,840 \pm 78$ AGN over 63.1 deg$^2$ ($7.8\pm0.1$%) using a threshold of $f_{\rm PSF} > 0.2$. We compare our DL-selected AGN with those identified in X-ray, mid-infrared (MIR), and optical spectroscopy and investigate 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. The AGN luminosity in the $I_{\rm E}$ filter correlates with host galaxy stellar mass, suggesting faster supermassive black hole (SMBH) growth in more massive galaxies. Moreover, the mean relative contribution of the AGN is higher in quiescent galaxies than in star-forming ones. Starburst galaxies and 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.

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Euclid Quick Data Release (Q1). The active galaxies of Euclid

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 $z_{\text{spec}}$, 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 ($JH$_$I_{\text{E}}Y$ and $I_{\text{E}}H$_$gz$) 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 $18<I_{\text{E}}\leq 24.5$, and a subsample of 30,422 candidates corresponding to an AGN surface density of 482 deg$^{-2}$ when limiting the depth to $18<I_{\text{E}}\leq 22$. The surface density of AGN recovered from this work is in line with predictions based on the AGN X-ray luminosity functions.

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