arXiv subjects

A. La Marca

Publications and source records attributed to A. La Marca.

17 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.

astro-ph.GA↗

arXiv2026

Euclid Quick Data Release (Q1). Searching for radio-selected \Euclid-dark galaxies in the EDF-N

We present and investigate the properties of a sample of radio-selected, Euclid-dark galaxies, identified from LOFAR HBA observations at 144 MHz within the Euclid Deep Field-North (EDF-N). Starting from radio sources lacking optical counterparts in previous surveys, but detected with Spitzer/IRAC, we identified 166 galaxies with no emission at a more than $3σ$ level in Euclid Quick Release 1 (Q1) images, and no matches in the Euclid Q1 catalogue. To minimise contamination from nearby sources, we selected a sub-sample of 88 isolated galaxies. By exploiting multi-band images and catalogues available for the EDF-N, we inferred the physical properties of our sample via SED fitting. The resulting redshift distribution spans $0.4 \leq z_\mathrm{ph} \leq 5.0$. We used recent sub-arcsecond imaging from the International LOFAR Telescope to constrain the nature of the compact radio emission through brightness temperature estimates. By combining this information with the radio excess relative to the infrared/radio correlation (IRRC), we searched for possible active galactic nuclei (AGN) activity. Approximately 40% of our sources show evidence of AGN activity. The Euclid-dark sources detected in the far-infrared are consistent with a population of heavily obscured, massive star-forming galaxies with high star formation rates. Their location above the star-forming main sequence is consistent with similar near-infrared-dark galaxy populations reported in the literature. We also performed a UV-to-radio median stacking analysis, finding that the two subsamples exhibit similar global physical properties and differ primarily in their radio emission. These preliminary results indicate that the wide area covered by Euclid enables the identification of a higher fraction of systems in which intense star formation and AGN activity coexist, likely capturing a key phase of galaxy--black hole co-evolution.

astro-ph.GA↗

arXiv2026

Advancing the detection of low surface brightness galaxies. I. ATTILA: multi-tAsking deTecTIon tool for Lsb gAlaxies

Context. Ultra-diffuse galaxies (UDGs) lie at the extreme end of the size-luminosity distribution of low surface-brightness (LSB) galaxies. Their detection and characterization require deep imaging and reliable source detection techniques that can handle low signal-to-noise ratios and severe source blending. Aims. We aim at improving the detection and characterization of the LSB galaxies and UDG candidates in different environments. To this end, we have developed a new automated detection Python-based tool, named ATTILA. Methods. We use deep g- and r-band imaging from the VST Early-type GAlaxy Survey (VEGAS), covering the central region of Hydra I and three new additional fields. Sources are identified combining tiling processing, source detection, and iterative deblending. The structural parameters are derived through surface brightness profile analysis and Sérsic modelling. Cluster membership is determined using the early-type galaxies colour-magnitude relation. Results. We identify 24 new UDGs, doubling the known population in the Hydra-I cluster to 48, consistent with expectations from halo mass scaling relations, and 92 additional LSB galaxies. In real data, ATTILA recovers more than 80% of previously known LSB galaxies and significantly improves the automated detection rate relative to standard methods. Conclusions. By improving the recovery of faint and diffuse sources while mitigating blending and contamination effects, ATTILA enables a more complete census of the LSB galaxy population, including UDGs.

astro-ph.GA↗

arXiv2026

The major merger-active galactic nucleus connection up to the cosmic noon

Galaxy major mergers are a potential mechanism for triggering active galactic nuclei (AGN) activity, but their role remains debated, particularly beyond the local Universe. We aim to shed light on the merger-AGN connection at $z=0.5$-$2$, exploiting the multi-wavelength datasets and {\it James Webb Space Telescope} (JWST) observations in the COSMOS field. We construct a stellar mass-limited sample and identify AGN via mid-infrared (MIR) colours, X-ray detections, and spectral energy distribution (SED) fitting. We train convolutional neural networks to identify mergers with mock JWST observations. We create non-AGN and non-merger control samples matching the redshift, stellar mass, and star-formation rate distributions of the AGN and mergers. We find AGN to be moderately more frequent in mergers than in non-mergers, with excess ratios ranging from $\sim2.5$ (X-ray AGN) to $\sim1.3$ (MIR) and $\sim 1.1$-1.2 (SED AGN). Similarly, AGN galaxies show a higher merger fraction ($f_{merg}$) than non-AGN controls. We then study $f_{merg}$ as a function of relative and absolute AGN power, utilising the AGN fraction ($f_{AGN}$) and accretion disc luminosity (L$_{disc}$) parameters. We uncover a $f_{merg}$-$f_{AGN}$ relation with two regimes: $f_{merg}$ stays roughly flat for less-dominant AGN ($f_{AGN}<0.8$) but increases at $f_{AGN}>0.8$ for the MIR and X-ray AGN, and more gently for SED AGN, where mergers appear to be the main triggering mechanism. Additionally, $f_{merg}$ increases monotonically as a function of L$_{disc}$, for all AGN types, reaching $f_{merg}>50\%$ for the most luminous AGN (L$_{disc} \gtrsim 10^{46}\,{erg\,s^{-1}}$). Overall, our results suggest that major mergers can trigger AGN out to the cosmic noon at $z\sim2$. Furthermore, the role of major mergers shows a clear dependence on AGN luminosity and remains the principal mechanism for fuelling the most powerful AGN.

astro-ph.GA↗

arXiv2026

Galaxy mergers classification using CNNs trained on Sérsic models, residuals and raw images

Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods such as Convolutional Neural Networks (CNNs) are essential for efficient detection. It is understood that CNNs classify mergers by identifying deviations from the regular, expected shapes of galaxies, particularly faint features that are indicative of a merger event. In this work, we present a novel investigation of the relative importance of different morphological components, namely faint residual features and position and spatial structure, in CNN-based binary classification of galaxies into merger and non-merger classes. Using mock images from the IllustrisTNG simulations processed to mimic Hyper Suprime-Cam (HSC) observations, we fit Sérsic profiles to each galaxy and generate three datasets: original images, model images containing only smooth Sérsic profiles, and residual images highlighting faint features after model subtraction. We train three identical CNNs on these datasets: CNN1 on original images, CNN2 on model images, and CNN3 on residual images. CNN1, trained on full images, achieves the highest accuracy of 74 percent. CNN2, using only shape information including source position, achieves 70 percent, while CNN3, using only faint residual features, achieves 68 percent. We find that galaxy merger classification is possible using either faint features or the position and Sérsic profile information present in residual and model images, respectively. Our results demonstrate that not only faint features but also source position information play complementary roles in merger classification. This has important implications for the design and interpretation of machine learning methods for galaxy morphology, particularly in regimes where specific image components may be enhanced or suppressed.

astro-ph.GA↗

arXiv2026

Galactic bars and active galactic nucleus fuelling in the second half of cosmic history

We investigate the role of galactic bars in fuelling and triggering Active Galactic Nucleus (AGN) in disc galaxies up to $z\sim 0.8$. We utilise a Deep Learning model, fine-tuned on Galaxy Zoo volunteer classifications, to identify (strongly and weakly) barred and unbarred disc galaxies in Hyper Suprime-Cam Subaru Strategic Program $i$-band images. We select AGN using three independent diagnostics: mid-infrared colours, X-ray detections, and spectral energy distribution (SED) fitting. The SED analysis, performed using CIGALE, quantifies the relative AGN contribution to the total galaxy luminosity ($f_{\rm AGN}$) and the AGN luminosity ($L_{\rm disc}$). We assess the impact of bars by comparing AGN incidence and properties in barred galaxies against carefully constructed redshift-, stellar mass-, and colour-matched unbarred control samples. Our binary AGN classification experiment demonstrates that barred disc galaxies host a higher fraction of AGN compared to their unbarred counterparts, though the significance depends on the AGN selection method, with a more modest excess for SED AGN, and control sample size. This suggests a contributing role for bars in the global AGN budget. The contribution of bars to AGN fuelling appears confined to systems where the AGN has a lower relative contribution to the host galaxy's emission ($f_{\rm AGN} < 0.75$). Crucially, we find a significant dearth of barred disc galaxies hosting AGN with $f_{\rm AGN} > 0.75$, independent of bar strength. Consistent with this, the fraction of barred galaxies among AGN hosts decreases with increasing $L_{\rm disc}$. Combined with previous results, we suggest that bars may contribute to fuelling the population of low-to-moderate luminosity AGN, but major mergers are the principal mechanism for triggering the most powerful and dominant accretion events.

astro-ph.GA↗

arXiv2025

Euclid: Quick Data Release (Q1)- The connection between galaxy close encounters and radio activity

Using the large statistics provided by both Euclid and the LOFAR surveys, we present the first large-scale study of the connection between 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 (AGN) and star-forming galaxies, we find that radio-emitting AGN show a clear preference to reside within galaxies undergoing a merging event. This is more significant for AGN that present extended and/or complex radio emission: indeed, about half of them are associated with merging systems, while only 15% are hosted by an isolated galaxy. The observed trend is primarily driven by AGN residing at z < 1, especially in the case of high - P144MHz > 10^24 W Hz-1 sr-1 - radio luminosities (60% in mergers versus 10% isolated regardless of radio appearance). The situation is reversed in the case of radio-emitting star-forming galaxies, which are preferentially associated with isolated systems. This is more significant as we move towards low radio-luminosity/star-formation objects (P144MHz < 10^23 W Hz-1 sr-1) for which we find 40% in isolated systems versus 20% in mergers. These values hold regardless of redshift. We interpret the above result for AGN 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 in the local Universe galaxies 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 prefer to be associated with isolated systems at all redshifts probed. (abridged)

astro-ph.GA↗

arXiv2025

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

Galaxy major mergers are a key pathway to trigger AGN. We present the first detection of major mergers in the Euclid Deep Fields and analyse their connection with AGN. We constructed a stellar-mass-complete ($M_*>10^{9.8}\,M_{\odot}$) sample of galaxies from the first quick data release (Q1), in the redshift range z=0.5-2. We selected AGN using X-ray data, optical spectroscopy, mid-infrared colours, and processing \IE observations with an image decomposition algorithm. We used CNNs trained on cosmological 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. Likewise, a generally larger merger fraction ($f_{merg}$) is seen in active galaxies than in the non-active controls. We analysed $f_{merg}$ as a function of the AGN bolometric luminosity ($L_{bol}$) and the contribution of the point-source to the total galaxy light in the \IE-band ($f_{PSF}$) as a proxy for the relative AGN contribution fraction. We uncovered a rising $f_{merg}$, with increasing $f_{PSF}$ up to $f_{PSF}=0.55$, after which we observed a decreasing trend. We then derived the point-source luminosity ($L_{PSF}$) and showed that $f_{merg}$ monotonically increases as a function of $L_{PSF}$ at z<0.9, with $f_{merg}>$50% for $L_{PSF}>2\,10^{43}$ erg/s. At z>0.9, $f_{merg}$ rises as a function of $L_{PSF}$, though mergers do not dominate until $L_{PSF}=10^{45}$ erg/s. For X-ray and spectroscopic AGN, we computed $L_{bol}$, which has a positive correlation with $f_{merg}$ for X-ray AGN, while shows a less pronounced trend for spectroscopic AGN due to the smaller sample size. At $L_{bol}>10^{45}$ erg/s, AGN mostly reside in mergers. We concluded that mergers are strongly linked to the most powerful, dust-obscured AGN, associated with rapid supermassive black hole growth.

astro-ph.GA↗

arXiv2025

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.

astro-ph.GA↗

arXiv2025

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.

astro-ph.HE↗

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.

astro-ph.GA↗

arXiv2024

Dust and Power: Unravelling the merger - active galactic nucleus connection in the second half of cosmic history

Galaxy mergers represent a fundamental physical process under hierarchical structure formation, but their role in triggering AGNs is still unclear. We aim to investigate the merger-AGN connection using state-of-the-art observations and novel methods in detecting mergers and AGNs. We selected stellar mass-limited samples at redshift z<1 from KiDS, focusing on the KiDS-N-W2 field with a wide range of multi-wavelength data. Three AGN types, selected in the MIR, X-ray, and via SED modelling, were analysed. To identify mergers, we used convolutional neural networks trained on two cosmological simulations. We created mass and redshift-matched control samples of non-mergers and non-AGNs. We observe a clear AGN excess (a factor of 2-3) in mergers with respect to non-mergers for the MIR AGNs, and a mild excess for the X-ray and SED AGNs, indicating that mergers could trigger all 3 types but are more connected with the MIR AGNs. About half of the MIR AGNs are in mergers but it is unclear whether mergers are the main trigger. For the X-ray and SED AGNs, mergers are unlikely to be the dominant trigger. We also explore the relation using the continuous AGN fraction $f_{AGN}$ parameter. Mergers exhibit a clear excess of high $f_{AGN}$ values relative to non-mergers, for all AGNs. We unveil the first merger fraction $f_{merg}-f_{AGN}$ relation with two distinct regimes. When the AGN is not dominant, the relation is only mildly increasing or even flat, with the MIR AGNs showing the highest $f_{merg}$. In the regime of very dominant AGNs ($f_{AGN}\geq0.8$), $f_{merg}$ shows a steeply rising trend with increasing $f_{AGN}$ for all AGN types. These trends are also seen when plotted against AGN bolometric luminosity. We conclude that mergers are most connected with dust-obscured AGNs (linked to a fast-growing phase of the SMBH) and are the main or even the sole fuelling mechanism of the most powerful AGNs.

astro-ph.GA↗

arXiv2024

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1<z<0.3$, precision and recall range between $\sim$70\% and 80\%, both of which decrease with increasing $z$ as expected (by $\sim$5\% for precision and $\sim$10\% for recall at $0.76<z<1.0$). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by $\sim$20-40\% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, all methods agree well with visual labels of clear mergers but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the multi-class classification task to distinguish pre-, post- and non-mergers, none of the methods offer a good performance, which could be partly due to limitations in resolution and depth of the data. With the advent of better quality data (e.g. JWST and Euclid), it is important to improve our ability to detect mergers and distinguish between merger stages.

astro-ph.GA↗

arXiv2024

Object classification with Convolutional Neural Networks: from KiDS to Euclid

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

astro-ph.IM↗

arXiv2023

A multi-band AGN-SFG classifier for extragalactic radio surveys using machine learning

Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet Active Galactic Nuclei (AGNs) are more common at fainter flux densities. Our aim is to develop a machine learning classifier that can efficiently and reliably separate AGNs and SFGs in radio continuum surveys. We perform supervised classification of SFGs vs AGNs using the Light Gradient Boosting Machine (LGBM) on three LOFAR Deep Fields (Lockman Hole, Bootes and ELAIS-N1), which benefit from a wide range of high-quality multi-wavelength data and classification labels derived from extensive spectral energy distribution (SED) analyses. Our trained model has a precision of 0.92(0.01) and a recall of 0.87(0.02) for SFGs. For AGNs, the model has slightly worse performance, with a precision of 0.87(0.02) and recall of 0.78(0.02). These results demonstrate that our trained model can successfully reproduce the classification labels derived from detailed SED analysis. The model performance decreases towards higher redshifts, mainly due to smaller training sample sizes. To make the classifier more adaptable to other radio galaxy surveys, we also investigate how our classifier performs with a poorer multi-wavelength sampling of the SED. In particular, we find that the far-infrared (FIR) and radio bands are of great importance. We also find that higher S/N in some photometric bands leads to a significant boost in the model's performance. In addition to using the 150 MHz radio data, our model can also be used with 1.4 GHz radio data. Converting 1.4 GHz to 150 MHz radio data reduces performance by about 4% in precision and 3% in recall. The final trained model is publicly available at https://github.com/Jesper-Karsten/MBASC

astro-ph.GA↗

arXiv2022

Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper by Merlin et al. focuses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes DeepLeGATo, Galapagos-2, Morfometryka, Profit and SourceXtractor++ on a sample of about 1.5 million simulated galaxies resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about 23 in one component and 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5 respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the Euclid official Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.

astro-ph.GA↗

arXiv2020

The first detection of ultra-diffuse galaxies in the Hydra I cluster from VEGAS survey

In this paper we report on the discovery of 27 low-surface brightness galaxies, of which 12 are candidate ultra-diffuse galaxy (UDG) in the Hydra I cluster, based on deep observations taken as part of the VST Early-type Galaxy Survey (VEGAS). This first sample of UDG candidates in the Hydra I cluster represents an important step in our project that aims to enlarge the number of confirmed UDGs and, through study of statistically relevant samples, constrain the nature and formation of UDGs. This study presents the main properties of this class of galaxies in the Hydra I cluster. For all UDGs, we analyse the light and colour distribution, and provide a census of the globular cluster (GC) systems around them. Given the limitations of a reliable GC selection based on two relatively close optical bands only, we find that half of the UDG candidates have a total GC population consistent with zero. Of the other half, two galaxies have a total population larger than zero at 2$σ$ level. We estimate the stellar mass, the total number of GCs and the GC specific frequency ($S_N$). Most of the candidates span a range of stellar masses of $10^7-10^8$~M$_{\odot}$. Based on the GC population of these newly discovered UDGs, we conclude that most of these galaxies have a standard or low dark matter content, with a halo mass of $\leq 10^{10}$~M$_{\odot}$.

astro-ph.GA↗