arXiv subjects

Marc Huertas-Company

Publications and source records attributed to Marc Huertas-Company.

At least 91 records · Page 5Linked to original sources

arXiv2021

Emission Line Galaxies in the SHARDS Frontier Fields I: Candidate Selection and the Discovery of Bursty Hα Emitters

Emission line galaxies provide a crucial tool for the study of galaxy formation and evolution, providing a means to trace a galaxy's star formation history or metal enrichment, and to identify galaxies at a range of stellar masses. In this paper we present a study of emission line galaxies in the SHARDS Frontier Fields medium-band survey. Through detailed flux calibrations we combine the first results of the SHARDS-FF survey with existing Hubble Frontier Field data to select 1,098 candidate emission line galaxies from the Hubble Frontier Filed clusters Abell 370 and MACS J1149.5+2223. Furthermore, we implement this deep medium-band imaging to update photometric redshift estimates and stellar population parameters and discover 38 predominantly low-mass Hα emitters at redshifts 0.24 < z < 0.46. Overall, 27 of these sources have corresponding UV data from the Hubble Space Telescope which allows us to distinguish these sources and investigate the burstiness of their star formation histories. We find that more than 50% of our sample show an enhancement in Hα over UV, suggesting recent bursts in star formation on time scales of a few, to tens of megayears. We investigate these sources and find that they are typically low-mass disky galaxies with normal sizes. Their structures and star formation suggest that they are not undergoing mergers but are bursting due to alternative causes, such as gas accretion.

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arXiv2021

Capturing the physics of MaNGA galaxies with self-supervised Machine Learning

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing dimensionality and complexity of datasets, which challenge the traditional methods used to extract the physical information they contain. We present the use of a novel self-supervised Machine Learning method to visualize the multi-dimensional information on stellar population and kinematics in the MaNGA survey in a two dimensional plane. Our framework is insensitive to non-physical properties such as the size of integral field unit (IFU) and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when using exclusively information about the internal structure, galaxies naturally cluster into two well-known categories from a purely data driven perspective: rotating main-sequence disks and massive slow rotators, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear only as a third cluster if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

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arXiv2021

Extending the evolution of the stellar mass-size relation at $z \leq 2$ to low stellar mass galaxies from HFF and CANDELS

We reliably extend the stellar mass-size relation over $0.2\leq z \leq2$ to low stellar mass galaxies by combining the depth of Hubble Frontier Fields (HFF) with the large volume covered by CANDELS. Galaxies are simultaneously modelled in multiple bands using the tools developed by the MegaMorph project, allowing robust size (i.e., half-light radius) estimates even for small, faint, and high redshift galaxies. We show that above 10$^7$M$_\odot$, star-forming galaxies are well represented by a single power law on the mass-size plane over our entire redshift range. Conversely, the stellar mass-size relation is steep for quiescent galaxies with stellar masses $\geq 10^{10.3}$M$_\odot$ and flattens at lower masses, regardless of whether quiescence is selected based on star-formation activity, rest-frame colours, or structural characteristics. This flattening occurs at sizes of $\sim1$kpc at $z\leq1$. As a result, a double power law is preferred for the stellar mass-size relation of quiescent galaxies, at least above 10$^7$M$_\odot$. We find no strong redshift dependence in the slope of the relation of star-forming galaxies as well as of high mass quiescent galaxies. We also show that star-forming galaxies with stellar masses $\geq$10$^{9.5}$M$_\odot$ and quiescent galaxies with stellar masses $\geq10^{10.3}$M$_\odot$ have undergone significant size growth since $z\sim2$, as expected; however, low mass galaxies have not. Finally, we supplement our data with predominantly quiescent dwarf galaxies from the core of the Fornax cluster, showing that the stellar mass-size relation is continuous below 10$^7$M$_\odot$, but a more complicated functional form is necessary to describe the relation.

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arXiv2021

The size function of massive satellites from the $R_e-R_h$ and $M_{star}-M_h$ relations: constraining the role of environment

In previous work we showed that a semi-empirical model in which galaxies in host dark matter haloes are assigned stellar masses via a stellar mass-halo mass (SMHM) relation and sizes ($R_e$) via a linear and tight $R_e-R_h$ relation, can faithfully reproduce the size function of local SDSS central galaxies and the strong size evolution of massive galaxies (MGs, $M_{\rm star}>10^{11.2}M_\odot$). In this third paper of the series, we focus on the population of satellite MGs. We find that without any additional calibration and irrespective of the exact SMHM relation, fraction of quenched galaxies or level of stellar stripping, the same model is able to reproduce the local size function of quiescent satellite MGs in SDSS. In addition, the same model can reproduce the puzzling weak dependence of mean size on host halo mass for both central and satellite galaxies. The model also matches the size function of starforming satellite MGs, after assuming that some of them transform into massive lenticulars in a few Gyr after infalling in the group/cluster environment. However, the vast majority of satellite lenticulars is predicted to form before infall. The $R_e-R_h$ appears to be fundamental to connect galaxies and their host haloes.

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arXiv2021

Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This setup provides 27 clusters created with this unsupervised learning which we show are well separated based on galaxy shape and structure (e.g., Sérsic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour-magnitude diagram, and span the range of scaling-relations such as mass vs. size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $\sim87\%$ is reached using an imbalanced dataset, matching real galaxy distributions, which includes 22.7\% early-type galaxies and 77.3\% late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.

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arXiv2021

A Deep Learning Approach for Characterizing Major Galaxy Mergers

Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end, we demonstrate a CNN-based regression model that is able to predict, for the first time, using a single image, the merger stage relative to the first perigee passage with a median error of 38.3 million years (Myrs) over a period of 400 Myrs. This model uses no specific dynamical modeling and learns only from simulated merger events. We show that our model provides reasonable estimates on real observations, approximately matching prior estimates provided by detailed dynamical modeling. We provide a preliminary interpretability analysis of our models, and demonstrate first steps toward calibrated uncertainty estimation.

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arXiv2020

The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view

Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the fine morphological details of galaxies from the sky background. We are able to \emph{quantitatively} identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is driven by small, more spheroidal, and quenched galaxies which are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched disky galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.

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arXiv2020

Anomaly Detection in Astronomical Images with Generative Adversarial Networks

We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in Hawai'i. The WGAN is trained on the entire sample, and learns to generate realistic HSC-like images that follow the distribution of the training data. We identify images which are less well-represented in the generator's latent space, and which the discriminator flags as less realistic; these are thus anomalous with respect to the rest of the data. We propose a new approach to characterize these anomalies based on a convolutional autoencoder (CAE) to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images. We construct a subsample of ~9,000 highly anomalous images from our nearly million object sample, and further identify interesting anomalies within these; these include galaxy mergers, tidal features, and extreme star-forming galaxies. The proposed approach could boost unsupervised discovery in the era of big data astrophysics.

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arXiv2020

CANDELS Meets GSWLC: Evolution of the Relationship Between Morphology and Star Formation Since z = 2

Galaxy morphology and its evolution over the cosmic epoch hold important clues for understanding the regulation of star formation (SF). However, studying the relationship between morphology and SF has been hindered by the availability of consistent data at different redshifts. Our sample, combining CANDELS (0.8 < z < 2.5) and the GALEX-SDSS-WISE Legacy Catalog (GSWLC; z ~ 0), has physical parameters derived using consistent SED fitting with flexible dust attenuation laws. We adopt visual classifications from Kartaltepe et al. 2015 and expand them to z ~ 0 using SDSS images matching the physical resolution of CANDELS rest-frame optical images and deep FUV GALEX images matching the physical resolution of the CANDELS rest-frame FUV images. Our main finding is that disks with SF clumps at z ~ 0 make a similar fraction (~15%) of star-forming galaxies as at z ~ 2. The clumpy disk contribution to the SF budget peaks at z ~ 1, rather than z ~ 2, suggesting that the principal epoch of disk assembly continues to lower redshifts. Star-forming spheroids ("blue nuggets"), though less centrally concentrated than quenched spheroids, contribute significantly (~15%) to the SF budget at z ~ 1-2, suggesting that compaction precedes quenching. Among green valley and quiescent galaxies, the pure spheroid fraction drops since z ~ 1, whereas spheroids with disks (S0-like) become dominant. Mergers at or nearing coalescence are enhanced in SFR relative to the main sequence at all redshifts by a factor of ~2, but contribute $\lesssim$5% to the SF budget, with their contribution remaining small above the main sequence.

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arXiv2020

The bivariate gas-stellar mass distributions and the mass functions of early- and late-type galaxies at $z\sim0$

We report the bivariate HI- and H$_2$-stellar mass distributions of local galaxies in addition of an inventory of galaxy mass functions, MFs, for HI, H$_2$, cold gas, and baryonic mass, separately into early- and late-type galaxies. The MFs are determined using the HI and H$_2$ conditional distributions and the galaxy stellar mass function, GSMF. For the conditional distributions we use the compilation presented in Calette et al. 2018. For determining the GSMF from $M_{\ast}\sim3\times10^{7}$ to $3\times10^{12}$ $M_{\odot}$, we combine two spectroscopic samples from the SDSS at the redshift range $0.0033<z<0.2$. We find that the low-mass end slope of the GSMF, after correcting from surface brightness incompleteness, is $α\approx-1.4$, consistent with previous determinations. The obtained HI MFs agree with radio blind surveys. Similarly, the H$_2$ MFs are consistent with CO follow-up optically-selected samples. We estimate the impact of systematics due to mass-to-light ratios and find that our MFs are robust against systematic errors. We deconvolve our MFs from random errors to obtain the intrinsic MFs. Using the MFs, we calculate cosmic density parameters of all the baryonic components. Baryons locked inside galaxies represent 5.4% of the universal baryon content, while $\sim96$% of the HI and H$_2$ mass inside galaxies reside in late-type morphologies. Our results imply cosmic depletion times of H$_2$ and total neutral H in late-type galaxies of $\sim 1.3$ and 7.2 Gyr, respectively, which shows that late type galaxies are on average inefficient in converting H$_2$ into stars and in transforming HI gas into H$_2$. Our results provide a fully self-consistent empirical description of galaxy demographics in terms of the bivariate gas--stellar mass distribution and their projections, the MFs. This description is ideal to compare and/or to constrain galaxy formation models.

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arXiv2020

Quenching as a Contest between Galaxy Halos and their Central Black Holes

Existing models of galaxy formation have not yet explained striking correlations between structure and star-formation activity in galaxies, notably the sloped and moving boundaries that divide star-forming from quenched galaxies in key structural diagrams. This paper uses these and other relations to ``reverse-engineer'' the quenching process for central galaxies. The basic idea is that star-forming galaxies with larger radii (at a given stellar mass) have lower black-hole masses due to lower central densities. Galaxies cross into the green valley when the cumulative effective energy radiated by their black hole equals $\sim4\times$ their halo-gas binding energy. Since larger-radii galaxies have smaller black holes, one finds they must evolve to higher stellar masses in order to meet this halo-energy criterion, which explains the sloping boundaries. A possible cause of radii differences among star-forming galaxies is halo concentration. The evolutionary tracks of star-forming galaxies are nearly parallel to the green-valley boundaries, and it is mainly the sideways motions of these boundaries with cosmic time that cause galaxies to quench. BH-scaling laws for star-forming, quenched, and green-valley galaxies are different, and most BH mass growth takes place in the green valley. Implications include: the radii of star-forming galaxies are an important second parameter in shaping their black holes; black holes are connected to their halos but in different ways for star-forming, quenched, and green-valley galaxies; and the same BH-halo quenching mechanism has been in place since $z \sim 3$. We conclude with a discussion of black hole-galaxy co-evolution, the origin and interpretation of BH scaling laws.

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arXiv2020

Detecting outliers in astronomical images with deep generative networks

With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging datasets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect $\sim90\%$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models.

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arXiv2019

Galaxy sizes and the galaxy-halo connection -- I: the remarkable tightness of the size distributions

The mass and structural assembly of galaxies is a matter of intense debate. Current theoretical models predict the existence of a linear relationship between galaxy size ($R_e$) and the host dark matter halo virial radius ($R_h$).\\ By making use of semi-empirical models compared to the size distributions of central galaxies from the Sloan Digital Sky Survey, we provide robust constraints on the normalization and scatter of the $R_e-R_h$ relation. We explore the parameter space of models in which the $R_e-R_h$ relation is mediated by either the spin parameter or the concentration of the host halo, or a simple constant the nature of which is in principle unknown. We find that the data require extremely tight relations for both early-type and late-type galaxies (ETGs,LTGs), especially for more massive galaxies. These constraints challenge models based solely on angular momentum conservation, which predict significantly wider distributions of galaxy sizes and no trend with stellar mass, if taken at face value. We discuss physically-motivated alterations to the original models that bring the predictions into better agreement with the data. We argue that the measured tight size distributions of SDSS disk galaxies can be reproduced by semi-empirical models in which the $R_e-R_h$ connection is mediated by the \emph{stellar} specific angular momenta $j_{star}.$ We find that current cosmological models of galaxy formation broadly agree with our constraints for LTGs, and justify the strong link between $R_e$ and $j_{star}$ that we propose, however the tightness of the $R_e-R_h$ relation found in such ab-initio theoretical models for ETGs is in tension with our semi-empirical findings.

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arXiv2019

Photometry of high-redshift blended galaxies using deep learning

The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50%. Current deblending approaches are in most cases either too slow or not accurate enough to reach the level of requirements. This work explores the use of deep neural networks to estimate the photometry of blended pairs of galaxies in monochrome space images, similar to the ones that will be delivered by the Euclid space telescope. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim$7% accuracy without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that forcing the network to simultaneously estimate a binary segmentation map results in a slightly improved photometry. All data products and codes will be made public to ease the comparison with other approaches on a common data set.

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arXiv2019

The Strong Gravitational Lens Finding Challenge

Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)

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arXiv2019

The structural properties of classical bulges and discs from z~2

We study the rest-frame optical mass-size relation of bulges and discs from z~2 to z~0 for a complete sample of massive galaxies in the CANDELS fields using 2 component Sérsic models (Dimauro et a. 2018). Discs and star forming galaxies follow similar mass-size relations. The mass-size relation of bulges is less steep than the one of quiescent galaxies (best fit slope of ~ 0.7 for quiescent galaxies against ~ 0.4 for bulges). We find little dependence of the structural properties of massive bulges and discs with the global morphology of galaxies (disc vs. bulge dominated) and the star formation activity (star-forming vs. quiescent). This result suggests similar bulge formation mechanisms for most massive galaxies and also that the formation of the bulge component does not significantly affect the disc structure. Our findings pose a challenge to models envisioning multiple channels for massive bulge growth, such as disc instabilities and mergers.

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arXiv2018

Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS

We present visual-like morphologies over 16 photometric bands, from ultra-violet to near infrared, for 8,412 galaxies in the Cluster Lensing And Supernova survey with Hubble (CLASH) obtained by a convolutional neural network (CNN) model. Our model follows the CANDELS main morphological classification scheme, obtaining the probability for each galaxy at each CLASH band of being spheroid, disk, irregular, point source, or unclassifiable. Our catalog contains morphologies for each galaxy with Hmag < 24.5 in every filter where the galaxy is observed. We trained an initial CNN model using approximately 7,500 expert eyeball labels from The Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). We created eyeball labels for 100 randomly selected galaxies per each of the 16-filters set of CLASH (1,600 galaxy images in total), where each image was classified by at least five of us. We use these labels to fine-tune the network in order to accurately predict labels for the CLASH data and to evaluate the performance of our model. We achieve a root-mean-square error of 0.0991 on the test set. We show that our proposed fine-tuning technique reduces the number of labeled images needed for training, as compared to directly training over the CLASH data, and achieves a better performance. This approach is very useful to minimize eyeball labeling efforts when classifying unlabeled data from new surveys. This will become particularly useful for massive datasets such as the ones coming from near future surveys such as EUCLID or the LSST. Our catalog consists of prediction of probabilities for each galaxy by morphology in their different bands and is made publicly available at http://www.inf.udec.cl/~guille/data/Deep-CLASH.csv.

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arXiv2018

A catalog of polychromatic bulge-disk decompositions of ~ 17.600 galaxies in CANDELS

Understanding how bulges grow in galaxies is critical step towards unveiling the link between galaxy morphology and star-formation. To do so, it is necessary to decompose large sample of galaxies at different epochs into their main components (bulges and disks). This is particularly challenging, especially at high redshifts, where galaxies are poorly resolved. This work presents a catalog of bulge-disk decompositions of the surface brightness profiles of ~17.600 H-band selected galaxies in the CANDELS fields (F160W<23, 0<z<2) in 4 to 7 filters covering a spectral range of 430-1600nm. This is the largest available catalog of this kind up to z = 2. By using a novel approach based on deep-learning to select the best model to fit, we manage to control systematics arising from wrong model selection and obtain less contaminated samples than previous works. We show that the derived structural properties are within $\sim10-20\%$ of random uncertainties. We then fit stellar population models to the decomposed SEDs (Spectral Energy Distribution) of bulges and disks and derive stellar masses (and stellar mass bulge-to-total ratios) as well as rest-frame colors (U,V,J) for bulges and disks separately. All data products are publicly released with this paper and through the web page https://lerma.obspm.fr/huertas/form_CANDELS and will be used for scientific analysis in forthcoming works.

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