arXiv subjects

B. Margalef-Bentabol

Publications and source records attributed to B. Margalef-Bentabol.

At least 19 recordsLinked to original sources

arXiv2026

Euclid Quick Data Release (Q1): Euclid spectroscopy of quasars. 1. Identification and redshift determination of 3500 bright quasars

The slitless spectroscopy mode of the NISP onboard Euclid has enabled efficient spectroscopy of objects within a large FoV. We present a large and homogeneous sample of bright quasars identified from the Euclid Quick Data Release (Q1) by combining high-purity candidate selections from Gaia and WISE with the NISP spectra. Through visual inspection of the Euclid spectra of these quasar candidates, we identify approximately 3500 quasars with reliable redshifts at $0<z\lesssim 4.8$. Of these, 2686 are new spectroscopic identifications relative to existing public compilations. We generate the first Euclid composite spectrum of quasars covering rest-frame NUV to NIR wavelengths without telluric lines, which will be pivotal to NIR quasar spectral analysis. We obtain an empirical spectroscopic depth of $J_{\rm E}\lesssim 21.5$ and $H_{\rm E}\lesssim 21.3$ at the sensitivity of the Wide Field Survey, beyond which the number of securely identified quasars declines sharply. We analyse VIS morphologies using Sersic and CAS metrics, and a deep-learning PSF fraction to track nuclear dominance. At low redshift ($z<0.5$), obvious host structures are common and a single Sersic model fits about half of the sources; at intermediate redshift ($0.5<z<2$), the nuclear component dominates, with 90% of the Sersic fits saturating at the upper index limit. In this intermediate redshift regime, $f_{\rm PSF}$ is available, and we use it as a more reliable compactness measure than the single-Sersic and CAS parameters to quantify nuclear versus host emission. We also explore the novel Euclid NIR colour space and discuss the role of these quasars in refining AGN selection techniques for future Euclid data releases. The spectroscopic bright quasar catalogue of this work, and the composite quasar spectrum, will be available at https://cdsarc.cds.unistra.fr/. (abridged)

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.

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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

Observationally derived change in the star formation rate as mergers progress

Galaxy mergers can change the rate at which stars are formed. We can trace when these changes occur in simulations of galaxy mergers. However, for observed galaxies we do not know how the star formation rate (SFR) evolves along the merger sequence as it is difficult to probe the time before or after coalescence. We aim to derive how SFR changes in observed mergers throughout the merger sequence, from a statistical perspective. Merger times were estimated for observed galaxy mergers in the Kilo Degree Survey (KiDS) using a convolutional neural network (CNN). The CNN was trained on mock KiDS images created using IllustrisTNG data. The SFRs were derived from spectral energy density fitting to KiDS and VIKINGs data. To determine the change in SFR for the merging galaxies, each merging galaxy was matched and compared to ten comparable non-merging galaxies; matching each galaxy in redshift, stellar mass, and local density. Mergers see an increase in the SFR for galaxies from 300~Myr before the merger until coalescence, continuing until at least 200~Myr after the merger event. After this, there is a possibility that SFR activity in the mergers begins to decrease, but we need more data to better constrain our merger times and SFRs to confirm this. We find that more galaxies with higher stellar mass (M$_{\star}$) have greater SFR enhancement as they merge compared to lower-M$_{\star}$ galaxies. There is no clear trend of changing SFR enhancement as local density changes, but the least dense environments have the least SFR enhancement. The increasing SFR enhancement is likely due to the closer proximity of galaxies and the presence of more close passes as the time before the merger approaches 0~Myr, with the SFR slowing 200~Myr after the merger event.

astro-ph.GA↗

arXiv2025

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

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

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

Determining the time before or after a galaxy merger event

Aims. This work aims to reproduce the time before or after a merger event of merging galaxies from the IllustrisTNG cosmological simulation using machine learning. Methods. Images of merging galaxies were created in the u, g, r, and i bands from IllustrisTNG. The merger times were determined using the time difference between the last simulation snapshot where the merging galaxies were tracked as two galaxies and the first snapshot where the merging galaxies were tracked as a single galaxy. This time was then further refined using simple gravity simulations. These data were then used to train a residual network (ResNet50), a Swin Transformer (Swin), a convolutional neural network (CNN), and an autoencoder (using a single latent neuron) to reproduce the merger time. The full latent space of the autoencoder was also studied to see if it reproduces the merger time better than the other methods. This was done by reducing the latent space dimensions using Isomap, linear discriminant analysis (LDA), neighbourhood components analysis, sparse random projection, truncated singular value decomposition and uniform manifold approximation and projection. Results. The CNN is the best of all the neural networks. The performance of the autoencoder was close to the CNN, with Swin close behind the autoencoder. ResNet50 performed the worst. The LDA dimensionality reduction performed the best of the six methods used. The exploration of the full latent space produced worse results than the single latent neuron of the autoencoder. For the test data set, we found a median error of 190 Myr, comparable to the time separation between snapshots in IllustrisTNG. Galaxies more than $\approx$ 625 Myr before a merger have poorly recovered merger times, as well as galaxies more than $\approx$ 125 Myr after a merger event.

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↗

arXiv2022

The half mass radius of MaNGA galaxies: Effect of IMF gradients

Gradients in the stellar populations (SP) of galaxies -- e.g., in age, metallicity, stellar Initial Mass Function (IMF) -- can result in gradients in the stellar mass to light ratio, $M_*/L$. Such gradients imply that the distribution of the stellar mass and light are different. For old SPs, e.g., in early-type galaxies at $z\sim 0$, the $M_*/L$ gradients are weak if driven by variations in age and metallicity, but significantly larger if driven by the IMF. A gradient which has larger $M_*/L$ in the center increases the estimated total stellar mass ($M_*$) and reduces the scale which contains half this mass ($R_{e,*}$), compared to when the gradient is ignored. For the IMF gradients inferred from fitting MILES simple SP models to the H$_β$, $\langle$Fe$\rangle$, [MgFe] and TiO$_{\rm 2SDSS}$ absorption lines measured in spatially resolved spectra of early-type galaxies in the MaNGA survey, the fractional change in $R_{e,*}$ can be significantly larger than that in $M_*$, especially when the light is more centrally concentrated. The $R_{e,*}-M_*$ correlation which results is offset by 0.3 dex to smaller sizes compared to when these gradients are ignored. Comparisons with `quiescent' galaxies at higher-$z$ must account for evolution in SP gradients (especially age and IMF) and the light profile before drawing conclusions about how $R_{e,*}$ and $M_*$ evolve. The implied merging between higher-$z$ and the present is less contrived if $R_{e,*}/R_e$ at $z\sim 0$ is closer to our IMF-driven gradient calibration than to unity.

astro-ph.GA↗

arXiv2020

Galaxy properties as revealed by MaNGA. III. Kinematic profiles and stellar population gradients in S0s

This is the third paper of a series where we study the stellar population gradients (SP; ages, metallicities, $α$-element abundance ratios and stellar initial mass functions) of early type galaxies (ETGs) at $z\le 0.08$ from the MaNGA-DR15 survey. In this work we focus on the S0 population and quantify how the SP varies across the population as well as with galactocentric distance. We do this by measuring Lick indices and comparing them to stellar population synthesis models. This requires spectra with high signal-to-noise which we achieve by stacking in bins of luminosity (L$_r$) and central velocity dispersion ($σ_0$). We find that: 1) There is a bimodality in the S0 population: S0s more massive than $3\times 10^{10}M_\odot$ show stronger velocity dispersion and age gradients (age and $σ_r$ decrease outwards) but little or no metallicity gradient, while the less massive ones present relatively flat age and velocity dispersion profiles, but a significant metallicity gradient (i.e. [M/H] decreases outwards). Above $2\times10^{11}M_\odot$ the number of S0s drops sharply. These two mass scales are also where global scaling relations of ETGs change slope. 2) S0s have steeper velocity dispersion profiles than fast rotating elliptical galaxies (E-FRs) of the same luminosity and velocity dispersion. The kinematic profiles and stellar population gradients of E-FRs are both more similar to those of slow rotating ellipticals (E-SRs) than to S0s, suggesting that E-FRs are not simply S0s viewed face-on. 3) At fixed $σ_0$, more luminous S0s and E-FRs are younger, more metal rich and less $α$-enhanced. Evidently for these galaxies, the usual statement that 'massive galaxies are older' is not true if $σ_0$ is held fixed.

astro-ph.GA↗