arXiv subjects

M. Huertas-Company

Publications and source records attributed to M. Huertas-Company.

At least 37 records · Page 2Linked to original sources

arXiv2025

COSMOS-Web: The emergence of the Hubble Sequence

Leveraging the wide area coverage of the COSMOS-Web survey, we quantify the abundance of different morphological types from $z\sim 7$ with unprecedented statistics and establish robust constraints on the epoch of emergence of the Hubble sequence. We measure the global (spheroids, disk-dominated, bulge-dominated, peculiar) and resolved (stellar bars) morphologies for about 400,000 galaxies down to F150W=27 using deep learning, representing a two-orders-of-magnitude increase over previous studies. We then provide reference Stellar Mass Functions (SMFs) of different morphologies between $z\sim 0.2$ and $z\sim 7$ and best-fit parameters to inform models of galaxy formation. All catalogs and data are made publicly available. (a)At redshift z > 4.5, the massive galaxy population ($\log M_*/M_\odot>10$) is dominated by disturbed morphologies (~70%) -- even in the optical rest frame -- and very compact objects (~30%) with effective radii smaller than ~500pc. This confirms that a significant fraction of the star formation at cosmic dawn occurs in very dense regions, although the stellar mass for these systems could be overestimated.(b)Galaxies with Hubble-type morphologies -- including bulge and disk-dominated galaxies -- arose rapidly around $z\sim 4$ and dominate the morphological diversity of massive galaxies as early as $z\sim 3$. (c)Using stellar bars as a proxy, we speculate that stellar disks in massive galaxies might have been common (>50%) among the star-forming population since cosmic noon ($z\sim2$-2.5) and formed as early as $z\sim 7$ (d)Massive quenched galaxies are predominantly bulge-dominated from z~4 onward, suggesting that morphological transformations briefly precede or are simultaneous to quenching mechanisms at the high-mass end. (e) Low-mass ($\log M_*/M_\odot<10$) quenched galaxies are typically disk-dominated, pointing to different quenching routes in the two ends of the stellar mass spectrum from cosmic dawn.

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arXiv2025

Little impact of mergers and galaxy morphology on the production and escape of ionizing photons in the early Universe

Compact, star-forming galaxies with high star formation rate surface densities ($Σ_{\text{SFR}}$) are often efficient Lyman continuum (LyC) emitters at $z\leq 4.5$, likely as intense stellar feedback creates low-density channels that allow photons to escape. Irregular or disturbed morphologies, such as those resulting from mergers, can also facilitate LyC escape by creating anisotropic gas distributions. We investigate the influence of galaxy morphology on LyC production and escape at redshifts $5 \leq z \leq 7$ using observations from various \textit{James Webb Space Telescope} (JWST) surveys. Our sample consists of 436 sources, which are predominantly low-mass ($\sim 10^{8.15} M_\odot$), star-forming galaxies with ionizing photon efficiency ($ξ_{\rm ion}$) values consistent with canonical expectations. Since direct measurements of $f_{\rm esc}$ are not possible during the Epoch of Reionization (EoR), we predict $f_{\rm esc}$ for high-redshift galaxies by applying survival analysis to a subsample of LyC emitters from the Low-Redshift Lyman Continuum Survey (LzLCS), selected to be direct analogs of reionization-era galaxies. We find that these galaxies exhibit on average modest predicted escape fractions ($\sim 0.04$). Additionally, we assess the correlation between morphological features and LyC emission. Our findings indicate that neither $ξ_{\rm ion}$ nor the predicted $f_{\rm esc}$ values show a significant correlation with the presence of merger signatures. This suggests that in low-mass galaxies at $z \geq 5$, strong morphological disturbances are not the primary mechanism driving LyC emission and leakage. Instead, compactness and star formation activity likely play a more pivotal role in regulating LyC escape.

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arXiv2024

COSMOS-Web: stellar mass assembly in relation to dark matter halos across $0.2<z<12$ of cosmic history

We study the stellar mass function (SMF) and the co-evolution with dark matter halos via abundance matching in the largest redshift range to date $0.2 5$, we find increased abundances of massive (log$\, M_{\star}/M_{\odot}>10.5$) implying integrated star formation efficiencies (SFE) $ε_{\star}\equiv M_{\star}\, f_{\rm b}^{-1} M_{\rm halo}^{-1} \gtrsim 0.5$. We find a flattening of the SMF at the high-mass end that is better described by a double power law at $z>5.5$. At $z \lesssim 5.5$ it transitions to a Schechter law which coincides with the emergence of the first massive quiescent galaxies in the Universe. We trace the cosmic stellar mass density (SMD) and infer the star formation rate density (SFRD), which at $z>7.5$ agrees remarkably with recent \JWST{} UV luminosity function-derived estimates. However, at $z \lesssim 3.5$, we find significant tension ($\sim 0.3$ dex) with the cosmic star formation (SF) history from instantaneous SF measures, the causes of which remain poorly understood. We infer the stellar-to-halo mass relation (SHMR) and the SFE from abundance matching out to $z=12$, finding a non-monotonic evolution. The SFE has the characteristic strong dependence with mass in the range of $0.02 - 0.2$, and mildly decreases at the low mass end out to $z\sim3.5$. At $z\sim3.5$ the SFE increases sharply from $\sim 0.1$ to approach high SFE of $0.8-1$ by $z\sim 10$ for log$(M_{\rm h}/M_{\odot})\approx11.5$, albeit with large uncertainties. Finally, we use the SHMR to track the SFE and stellar mass growth throughout the halo history and find that they do not grow at the same rate -- from the earliest times up until $z\sim3.5$ the halo growth rate outpaces galaxy assembly, but at $z>3.5$ halo growth stagnates and accumulated gas reservoirs keep the SF going and galaxies outpace halos.

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arXiv2024

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

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.

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arXiv2024

Euclid. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breath of Euclid's data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific analyses. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z=3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of H_E<26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission's main cosmological analysis. (abridged)

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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.

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arXiv2024

A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning view

Supermassive black holes require a reservoir of cold gas at the centre of their host galaxy in order to accrete and shine as active galactic nuclei (AGN). Major mergers have the ability to drive gas rapidly inwards, but observations trying to link mergers with AGN have found mixed results due to the difficulty of consistently identifying galaxy mergers in surveys. This study applies deep learning to this problem, using convolutional neural networks trained to identify simulated post-merger galaxies from survey-realistic imaging. This provides a fast and repeatable alternative to human visual inspection. Using this tool, we examine a sample of ~8500 Seyfert 2 galaxies (L[OIII] ~ $10^{38.5 - 42}$ erg/s) at z < 0.3 in the Sloan Digital Sky Survey and find a merger fraction of $2.19_{-0.17}^{+0.21}$% compared with inactive control galaxies, in which we find a merger fraction of $2.96_{-0.20}^{+0.26}$%, indicating an overall lack of mergers among AGN hosts compared with controls. However, matching the controls to the AGN hosts in stellar mass and star formation rate reveals that AGN hosts in the star-forming blue cloud exhibit a ~$2\times$ merger enhancement over controls, while those in the quiescent red sequence have significantly lower relative merger fractions, leading to the observed overall deficit due to the differing $M_{\ast} - $SFR distributions. We conclude that while mergers are not the dominant trigger of all low-luminosity, obscured AGN activity in the nearby Universe, they are more important to AGN fuelling in galaxies with higher cold gas mass fractions as traced through star formation.

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arXiv2023

On the nature of disks at high redshift seen by JWST/CEERS with contrastive learning and cosmological simulations

Visual inspections of the first optical rest-frame images from JWST have indicated a surprisingly high fraction of disk galaxies at high redshifts. Here, we alternatively apply self-supervised machine learning to explore the morphological diversity at $z \geq 3$. Our proposed data-driven representation scheme of galaxy morphologies, calibrated on mock images from the TNG50 simulation, is shown to be robust to noise and to correlate well with the physical properties of the simulated galaxies, including their 3D structure. We apply the method simultaneously to F200W and F356W galaxy images of a mass-complete sample ($M_*/M_\odot>10^9$) at $ 3 \leq z \leq 6$ from the first JWST/NIRCam CEERS data release. We find that the simulated and observed galaxies do not exactly populate the same manifold in the representation space from contrastive learning. We also find that half the galaxies classified as disks -- either CNN-based or visually -- populate a similar region of the representation space as TNG50 galaxies with low stellar specific angular momentum and non-oblate structure. Although our data-driven study does not allow us to firmly conclude on the true nature of these galaxies, it suggests that the disk fraction at $z \geq 3$ remains uncertain and possibly overestimated by traditional supervised classifications. Deeper imaging and spectroscopic follow-ups as well as comparisons with other simulations will help to unambiguously determine the true nature of these galaxies, and establish more robust constraints on the emergence of disks at very high redshift.

astro-ph.CO↗

arXiv2023

The COSMOS-Web ring: in-depth characterization of an Einstein ring lensing system at z~2

Aims. We provide an in-depth analysis of the COSMOS-Web ring, an Einstein ring at z=2 that we serendipitously discovered in the COSMOS-Web survey and possibly the most distant lens discovered to date. Methods. We extract the visible and NIR photometry from more than 25 bands and we derive the photometric redshifts and physical properties of both the lens and the source with three different SED fitting codes. Using JWST/NIRCam images, we also produce two lens models to (i) recover the total mass of the lens, (ii) derive the magnification of the system, (iii) reconstruct the morphology of the lensed source, and (iv) measure the slope of the total mass density profile of the lens. Results. The lens is a very massive and quiescent (sSFR < 10^(-13) yr-1) elliptical galaxy at z = 2.02 \pm 0.02 with a total mass Mtot(<thetaE) = (3.66 \pm 0.36) x 10^11 Msun and a stellar mass M* = (1.37 \pm 0.14) x 10^11 Msun. Compared to SHMRs from the literature, we find that the total mass is consistent with the presence of a DM halo of mass Mh = 1.09^(+1.46)_(-0.57) x 10^13 Msun. In addition, the background source is a M* = (1.26 \pm 0.17) x 10^10 Msun star-forming galaxy (SFR=(78 \pm 15) Msun/yr) at z = 5.48 \pm 0.06. Its reconstructed morphology shows two components with different colors. Dust attenuation values from SED fitting and nearby detections in the FIR also suggest it could be partially dust-obscured. Conclusions. We find the lens at z=2. Its total, stellar, and DM halo masses are consistent within the Einstein ring, so we do not need any unexpected changes in our description of the lens (e.g. change its IMF or include a non-negligible gas contribution). The most likely solution for the lensed source is at z = 5.5. Its reconstructed morphology is complex and highly wavelength dependent, possibly because it is a merger or a main sequence galaxy with a heterogeneous dust distribution.

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arXiv2023

Ask The Machine: Systematic detection of wind-type outflows in low-mass X-ray binaries

The systematic discovery of outflows in the optical spectra of low-mass X-ray binaries opened a new avenue for the study of the outburst evolution in these extreme systems. However, the efficient detection of such features in a continuously growing database requires the development of new analysis techniques with a particular focus on scalability, adaptability, and automatization. In this pilot study, we explore the use of machine learning algorithms to perform the identification of outflows in spectral line profiles observed in the optical range. We train and test the classifier on a simulated database, constructed through a combination of disc emission line profiles and outflow signatures, emulating typical observations of low-mass X-ray binaries. The final, trained classifier is applied to two sets of spectra taken during two bright outbursts that were particularly well covered, those of V404 Cyg (2015) and MAXI J1820+070 (2018). The resulting classification gained by this novel approach is overall consistent with that obtained through traditional techniques, while it simultaneously provides a number of key advantages over the latter, including the access to low velocity outflows. This study sets the foundations for future studies on large samples of spectra from low-mass X-ray binaries and other compact binaries.

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arXiv2023

CEERS Key Paper VI: JWST/MIRI Uncovers a Large Population of Obscured AGN at High Redshifts

Mid-infrared observations are powerful in identifying heavily obscured Active Galactic Nuclei (AGN) which have weak emission in other wavelengths. Data from the Mid-Infrared Instrument (MIRI) onboard JWST provides an excellent opportunity to perform such studies. We take advantage of the MIRI imaging data from the Cosmic Evolution Early Release Science Survey (CEERS) to investigate the AGN population in the distant universe. We estimate the source properties of MIRI-selected objects by utilizing spectral energy distribution (SED) modelling, and classify them into star-forming galaxies (SF), SF-AGN mixed objects, and AGN. The source numbers of these types are 418, 111, and 31, respectively, from 4 MIRI pointings covering $\sim 9$ arcmin$^2$. The sample spans a redshift range of $\approx 0$--5. We derive the median SEDs for all three source types, respectively, and publicly release them. The median MIRI SED of AGN is similar to the typical SEDs of hot dust-obscured galaxies and Seyfert 2s, for which the mid-IR SEDs are dominantly from AGN-heated hot dust. Based on our SED-fit results, we estimate the black-hole accretion density (BHAD; i.e., total BH growth rate per comoving volume) as a function of redshift. At $z<3$, the resulting BHAD agrees with the X-ray measurements in general. At $z>3$, we identify a total of 27 AGN and SF-AGN mixed objects, leading to that our high-$z$ BHAD is substantially higher than the X-ray results ($\sim 0.5$ dex at $z \approx 3$--5). This difference indicates MIRI can identify a large population of heavily obscured AGN missed by X-ray surveys at high redshifts.

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arXiv2023

Galaxy Morphology from $z\sim6$ through the eyes of JWST

We analyze the Near Infrared ($\sim0.8-1μ$m) rest-frame morphologies of galaxies with $\log M_*/M_\odot>9$ in the redshift range $0 10.5$) at $z\sim5$, and bulge-dominated galaxies also exist at these early epochs, confirming a rich and evolved morphological diversity of galaxies $\sim1$ Gyr after the Big Bang. Finally, we find that the morphology-quenching relation is already in place for massive galaxies at $z>3$, with massive quiescent galaxies ($\log M_*/M_\odot>10.5$) being predominantly bulge-dominated.

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arXiv2023

Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks

Interactions between galaxies leave distinguishable imprints in the form of tidal features which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training $\sim$6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle and surface brightness ($μ_{lim}$ =26-35 mag arcsec$^{-2}$). We obtain satisfactory results with accuracy, precision and recall values of Acc=0.84, P=0.72 and R=0.85, respectively, for the test sample. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87% of the tidal streams; these fractions are below 75% for mergers, tidal tails and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data.

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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.

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arXiv2022

Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonnière et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double Sérsic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double Sérsic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)

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arXiv2022

Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks

We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of $\sim$21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN - monochromatic images versus $gri$-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES ($i<18$), while the other is trained with bright galaxies ($r<17.5$) and `emulated' galaxies up to $r$-band magnitude $22.5$. Despite the different approaches, the agreement between the two catalogues is excellent up to $i<19$, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on $gri$-band images, at least in the bright regime. At fainter magnitudes, $i>19$, the overall agreement is good ($\sim$95\%), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases we are able to identify lenticular galaxies (at least up to $i<19$), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.

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arXiv2022

A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM.

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arXiv2022

From naked spheroids to disky galaxies: how do massive disk galaxies shape their morphology?

We investigate the assembly history of massive disk galaxies and describe how they shape their morphology through cosmic time. Using SHARDS and HST data, we modeled the surface brightness distribution of 91 massive galaxies at redshift $0.14<z\leq 1$ in the wavelength range $0.5-1.6$ $μ$m, deriving the uncontaminated spectral energy distributions of their bulges and disks separately. This spectrophotometric decomposition allows us to compare the stellar populations properties of each component in individual galaxies. We find that the majority of massive galaxies ($\sim85\%$) builds inside-out, growing their extended stellar disk around the central spheroid. Some bulges and disks could start forming at similar epochs, but these bulges grow more rapidly than their disks, assembling $80\%$ of their mass in $\sim0.7$ Gyr and $\sim3.5$ Gyr, respectively. Moreover, we infer that both older bulges and older disks are more massive and compact than younger stellar structures. In particular, we find that bulges display a bimodal distribution of mass-weighted ages, i.e., they form in two waves. In contrast, our analysis of the disk components indicates that they form at $z\sim1$ for both first and second-wave bulges. This translates to first-wave bulges taking longer in acquiring a stellar disk ($5.2$ Gyr) compared to second-wave less-compact spheroids ($0.7$ Gyr). We do not find distinct properties (e.g., mass, star formation timescale, and mass surface density) for the disks in both types of galaxies. We conclude that the bulge mass and compactness mainly regulate the timing of the stellar disk growth, driving the morphological evolution of massive disk galaxies.

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