Proyecto de Investigación: EVOLUCION DE GALAXIAS
Cargando...
Colaboradores
Financiadores
ID
AYA2016-77846-P
Autores
Publicaciones
J-PLUS: The Javalambre Photometric Local Universe Survey
(EDP Sciences, 2019-02-21) Cenarro, A. J.; Moles, M.; Cristóbal Hornillos, D.; Marín Franch, A.; Ederoclite, A.; Varela, J.; López Sanjuan, C.; Hernández Monteagudo, C.; Angulo, R. E.; Vázquez Ramió, H.; Viironen, K.; Reis, R. R. R.; Molino, A.; Roig, F.; Vilella-Rojo, G.; Sako, M.; Sánchez Blázquez, P.; Gurung López, S.; Santos, W. A.; Telles, E.; Allende Prieto, C.; Bonatto, C.; Vilchez, J. M.; San Roman, I.; Daflon, S.; Dupke, R. A.; Greisel, N.; Jiménez Teja, Y.; Placco, V. M.; Logroño García, R.; Spinoso, D.; Maícas, N.; Izquierdo Villalba, D.; Abril, J.; Aguerri, J. A. L.; Carvano, J. M.; Bielsa de Toledo, S.; Chies Santos, A. L.; Falcón Barroso, J.; Civera, T.; Gonçalves, D. R.; Hernández Fuertes, J.; Iglesias Marzoa, R.; Whitten, D. D.; Antón, J. L.; Kruuse, K.; Lamadrid, J. L.; Bello, R.; Castillo Ramírez, J.; López Sainz, A.; Moreno Signes, A.; Chueca, S.; Díaz Martín, M. C.; Beers, T. C.; Domínguez Martínez, M.; Rueda Teruel, F.; Garzarán Calderaro, J.; Iñiguez, C.; Tilve, V.; Jiménez Ruiz, J. M.; Lasso Cabrera, N.; Alcaniz, J. S.; López Alegre, G.; Muniesa, D. J.; Lopes de Oliveira, R.; Tamm, A.; Rodríguez Llano, S.; Rueda Teruel, S.; Akras, S.; Alfaro, E. J.; Soriano Laguía, I.; Valdivielso, L.; Beasley, M. A.; Borges Fernandes, M.; Yanes Díaz, A.; Mendes de Oliveira, Claudia L.; Lyman, J. D.; Sodré, L.; Carrasco, J. M.; Coelho, P. R. T.; Xavier, H. S.; Costa Duarte, M. V.; Abramo, L. R.; Álvarez Candal, A.; Galarza, A.; Ascaso, B.; Bruzual, G.; González Serrano, J. I.; Gutiérrez Soto, L. A.; Buzzo, M. L.; Cepa, J.; Kuncarayakti, H.; Landim, R. C. G.; Cortesi, A.; De Prá, M.; Lima Neto, G. B.; Maíz Apellániz, J.; Favole, G.; Galbany, L.; Orsi, Álvaro A.; García, K.; Nogueira Cavalcante, J. P.; González Delgado, R. M.; Hernández Jiménez, J. A.; Oteo, I.; Kanaan, A.; Laur, J.; Rebassa-Mansergas, A.; Lincandro, J.; Miralda Escudé, J.; Salvador Rusiñol, N.; Sampedro, L.; Morate, D.; Novais, P. M.; Schmidtobreick, L.; Siffert, B. B.; Oncins, M.; Overzier, R. A.; Bonoli, S.; Hurier, G.; Pereira, C. B.; Díaz García, Pedro; Solano, Enrique; Gobierno de Aragón; European Commission (EC); Conselho Nacional de Desenvolvimento Científico e Tecnológico; Financiadora de Estudos e Projetos (FINEP); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); National Science Foundation (NSF); Ministerio de Economía y Competitividad (MINECO); 0000-0002-2573-2342; Jailson Souza de Alcaniz. [https://orcid.org/0000-0003-2441-1413]; Coelho, P. R. T. [0000-0003-1846-4826]; Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737
The Javalambre Photometric Local Universe Survey (J-PLUS ) is an ongoing 12-band photometric optical survey, observing thousands of square degrees of the Northern Hemisphere from the dedicated JAST/T80 telescope at the Observatorio Astrofísico de Javalambre (OAJ). The T80Cam is a camera with a field of view of 2 deg2 mounted on a telescope with a diameter of 83 cm, and is equipped with a unique system of filters spanning the entire optical range (3500–10 000 Å). This filter system is a combination of broad-, medium-, and narrow-band filters, optimally designed to extract the rest-frame spectral features (the 3700–4000 Å Balmer break region, Hδ, Ca H+K, the G band, and the Mg b and Ca triplets) that are key to characterizing stellar types and delivering a low-resolution photospectrum for each pixel of the observed sky. With a typical depth of AB ∼21.25 mag per band, this filter set thus allows for an unbiased and accurate characterization of the stellar population in our Galaxy, it provides an unprecedented 2D photospectral information for all resolved galaxies in the local Universe, as well as accurate photo-z estimates (at the δ z/(1 + z)∼0.005–0.03 precision level) for moderately bright (up to r ∼ 20 mag) extragalactic sources. While some narrow-band filters are designed for the study of particular emission features ([O II]/λ3727, Hα/λ6563) up to z < 0.017, they also provide well-defined windows for the analysis of other emission lines at higher redshifts. As a result, J-PLUS has the potential to contribute to a wide range of fields in Astrophysics, both in the nearby Universe (Milky Way structure, globular clusters, 2D IFU-like studies, stellar populations of nearby and moderate-redshift galaxies, clusters of galaxies) and at high redshifts (emission-line galaxies at z ≈ 0.77, 2.2, and 4.4, quasi-stellar objects, etc.). With this paper, we release the first ∼1000 deg2 of J-PLUS data, containing about 4.3 million stars and 3.0 million galaxies at r < 21 mag. With a goal of 8500 deg2 for the total J-PLUS footprint, these numbers are expected to rise to about 35 million stars and 24 million galaxies by the end of the survey.
A few StePS forward in unveiling the complexity of galaxy evolution: light-weighted stellar ages of intermediate-redshift galaxies with WEAVE
(EDP Sciences, 2019-11-21) Costantin, L.; Lovino, A.; Zibetti, S.; Longhetti, M.; Gallazzi, A.; Mercurio, A.; Lonoce, I.; Balcells, M.; Bolzonella, M.; Busarello, G.; Dalton, G.; Ferré Mateu, A.; García Benito, R.; Gargiulo, A.; Haines, C.; Jin, S.; La Barbera, F.; McGee, S.; Merluzzi, P.; Morelli, L.; Murphy, D. N. A.; Peralta de Arriba, L.; Pizzella, A.; Poggianti, B. M.; Pozzetti, L.; Sánchez Blázquez, P.; Talia, M.; Tortora, C.; Trager, S. C.; Vazdekis, A.; Vergani, D.; Vulcani, B.; Istituto Nazionale di Astrofisica (INAF); Comunidad de Madrid; Fundación Caixa; Agencia Estatal de Investigación (AEI); Vulcani, B. [0000-0003-0980-1499]; De Arribas, L. P. [0000-0002-3084-084X]; Zibetti, S. [0000-0003-1734-8356]; Talia, M. [0000-0003-4352-2063]; Tortora, C. [0000-0001-7958-6531]; Pizzella, A. [0000-0001-9585-417X]; Ferré Mateu, A. [0000-0002-6411-220X]; McGee, S. [0000-0003-3255-3139]; Gargiulo, A. [0000-0002-3351-1216]; Longhetti, M. [0000-0002-6142-4822]; Gallazzi, A. [0000-0002-9656-1800]; Vergani, D. [0000-0003-0898-2216]; Haines, C. [0000-0002-8814-8960]; Costantin, L. [0000-0001-6820-0015]; Pozzetti, L. [0000-0001-7085-0412]; Dalton, G. [0000-0002-3031-2588]; Iovino, A. [0000-0001-6958-0304]; Sánchez Blázquez, P. [0000-0003-0651-0098]; Merluzzi, P. [0000-0003-3966-2397]; Centros de Excelencia Severo Ochoa, INSTITUTO DE ASTROFISICA DE ANDALUCIA (IAA), SEV-2017-0709; Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737
Context. The upcoming new generation of optical spectrographs on four-meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide invaluable information for reconstructing the history of star formation in individual galaxies up to redshifts of about 0.7.
Aims. We aim at defining simple but robust and meaningful physical parameters that can be used to trace the coexistence of widely diverse stellar components: younger stellar populations superimposed on the bulk of older ones.
Methods. We produced spectra of galaxies closely mimicking data from the forthcoming Stellar Populations at intermediate redshifts Survey (StePS), a survey that uses the WEAVE spectrograph on the William Herschel Telescope. First, we assessed our ability to reliably measure both ultraviolet and optical spectral indices in galaxies of different spectral types for typically expected signal-to-noise ratios. We then analyzed such mock spectra with a Bayesian approach, deriving the probability density function of r- and u-band light-weighted ages as well as of their difference.
Results. We find that the ultraviolet indices significantly narrow the uncertainties in estimating the r- and u-band light-weighted ages and their difference in individual galaxies. These diagnostics, robustly retrievable for large galaxy samples even when observed at moderate signal-to-noise ratios, allow us to identify secondary episodes of star formation up to an age of ∼0.1 Gyr for stellar populations older than ∼1.5 Gyr, pushing up to an age of ∼1 Gyr for stellar populations older than ∼5 Gyr.
Conclusions. The difference between r-band and u-band light-weighted ages is shown to be a powerful diagnostic to characterize and constrain extended star-formation histories and the presence of young stellar populations on top of older ones. This parameter can be used to explore the interplay between different galaxy star-formation histories and physical parameters such as galaxy mass, size, morphology, and environment.
Hierarchical Bayesian approach for estimating physical properties in nearby galaxies: Age Maps (Paper II)
(Oxford Academics: Oxford University Press, 2019-02-15) Sánchez Gil, M. C.; Alfaro, Emilio J.; Cerviño, M.; Pérez, E.; Bland Hawthorn, J.; Death Jones, D.; Ministerio de Economía y Competitividad (MINECO); Junta de Andalucia; Agencia Estatal de Investigación (AEI); 0000-0002-2234-7035; 0000-0001-8009-231X; 0000-0001-9737-4559; Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737
One of the fundamental goals of modern astrophysics is to estimate the physical parameters of galaxies. We present a hierarchical Bayesian model to compute age maps from images in the H α line (taken with Taurus tunable filter, TTF), ultraviolet band (GALEX far UV, FUV), and infrared bands (Spitzer 24, 70, and 160 μm). We present the burst ages for young stellar populations in a sample of nearby and nearly face-on galaxies. The H α to FUV flux ratio is a good relative indicator of the very recent star formation history (SFH). As a nascent star-forming region evolves, the H α line emission declines earlier than the UV continuum, leading to a decrease in the H α/FUV ratio. Using star-forming galaxy models, sampled with a probabilistic formalism, and allowing for a variable fraction of ionizing photons in the clusters, we obtain the corresponding theoretical ratio H α/FUV to compare with our observed flux ratios, and thus to estimate the ages of the observed regions. We take into account the mean uncertainties and the interrelationships between parameters when computing H α/FUV. We propose a Bayesian hierarchical model where a joint probability distribution is defined to determine the parameters (age, metallicity, IMF) from the observed data (the observed flux ratios H α/FUV). The joint distribution of the parameters is described through independent and identically distributed (i.i.d.) random variables generated through MCMC (Markov Chain Monte Carlo) techniques.
Stellar populations of galaxies in the ALHAMBRA survey up to z ∼ 1 IV. Properties of quiescent galaxies on the stellar mass–size plane
(EDP Sciences, 2019-11-13) Cenarro, A. J.; López Sanjuan, C.; Peralta de Arriba, L.; Ferreras, I.; Cerviño, M.; Márquez, I.; Masegosa, J.; Del Olmo, A.; Perea, J.; Díaz García, Pedro; Gobierno de Aragón; Ministry of Science and Technology of Taiwan (MOST); Academia Sinica; Ministerio de Ciencia e Innovación (MICINN); Generalitat Valenciana; Junta de Andalucía; Generalitat de Catalunya; Ministerio de Economía y Competitividad (MINECO); Cerviño, M. [0000-0001-8009-231X]; De Arribas, L. P. [0000-0002-3084-084X]; López Sanjuan, C. [0000-0002-5743-3160]; Márquez Pérez, I. [0000-0003-2629-1945]; Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737
Aims. We perform a comprehensive study of the stellar population properties (formation epoch, age, metallicity, and extinction) of quiescent galaxies as a function of size and stellar mass to constrain the physical mechanism governing the stellar mass assembly and the likely evolutive scenarios that explain their growth in size.
Methods. After selecting all the quiescent galaxies from the ALHAMBRA survey by the dust-corrected stellar mass–colour diagram, we built a shared sample of ∼850 quiescent galaxies with reliable measurements of sizes from the HST. This sample is complete in stellar mass and luminosity, I ≤ 23. The stellar population properties were retrieved using the fitting code for spectral energy distributions called MUlti-Filter FITting for stellar population diagnostics (MUFFIT) with various sets of composite stellar population models. Age, formation epoch, metallicity, and extinction were studied on the stellar mass–size plane as function of size through a Monte Carlo approach. This accounted for uncertainties and degeneracy effects amongst stellar population properties.
Results. The stellar population properties of quiescent galaxies and their stellar mass and size since z ∼ 1 are correlated. At fixed stellar mass, the more compact the quiescent galaxy, the older and richer in metals it is (1 Gyr and 0.1 dex, respectively). In addition, more compact galaxies may present slight lower extinctions than their more extended counterparts at the same stellar mass (< 0.1 mag). By means of studying constant regions of stellar population properties across the stellar mass–size plane, we obtained empirical relations to constrain the physical mechanism that governs the stellar mass assembly of the form M⋆ ∝ rcα, where α amounts to 0.50–0.55 ± 0.09. There are indications that support the idea that the velocity dispersion is tightly correlated with the stellar content of galaxies. The mechanisms driving the evolution of stellar populations can therefore be partly linked to the dynamical properties of galaxies, along with their gravitational potential.
The miniJPAS survey: star-galaxy classification using machine learning
(EDP Sciences, 2021-01-18) Baqui, P. O.; Marra, V.; Casarini, L.; Angulo, R.; Hernández Monteagudo, C.; Lopes, P. A. A.; López Sanjuan, C.; Muniesa, D. J.; Placco, V. M.; Quartin, M.; Queiroz, C.; Sobral, D.; Tempel, E.; Varela, J.; Vílchez, J. M.; Abramo, L. R.; Alcaniz, J. S.; Benítez, N.; Bonoli, S.; Carneiro, S.; Cenarro, A. J.; Cristóbal Hornillos, D.; De Amorim, A. L.; De Oliveira, C. M.; Dupke, R. A.; Ederoclite, A.; González Delgado, R. M.; Marín Franch, A.; Moles, M.; Vázquez Ramió, H.; Sodré, L.; Taylor, K.; Solano, Enrique; Díaz García, Pedro; European Commission (EC); Agencia Estatal de Investigación (AEI); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Gobierno de Aragón; Ministerio de Ciencia e Innovación (MICINN); Ministerio de Economía y Competitividad (MINECO); Ministry of Education, Culture, Sports, Science and Technology (MEXT); 0000-0002-7773-1579
Context. Future astrophysical surveys such as J-PAS will produce very large datasets, the so-called “big data”, which will require the deployment of accurate and efficient machine-learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about ∼1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. The miniJPAS primary catalog contains approximately 64 000 objects in the r detection band (magAB ≲ 24), with forced-photometry in all other filters.
Aims. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g., stars) objects, which is a step required for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools that are based on explicit modeling. In particular, our goal is to release a value-added catalog with our best classification.
Methods. In order to train and test our classifiers, we cross-matched the miniJPAS dataset with SDSS and HSC-SSP data, whose classification is trustworthy within the intervals 15 ≤ r ≤ 20 and 18.5 ≤ r ≤ 23.5, respectively. We trained and tested six different ML algorithms on the two cross-matched catalogs: K-nearest neighbors, decision trees, random forest (RF), artificial neural networks, extremely randomized trees (ERT), and an ensemble classifier. This last is a hybrid algorithm that combines artificial neural networks and RF with the J-PAS stellar and galactic loci classifier. As input for the ML algorithms we used the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also used the mean point spread function in the r detection band for each pointing.
Results. We find that the RF and ERT algorithms perform best in all scenarios. When the full magnitude range of 15 ≤ r ≤ 23.5 is analyzed, we find an area under the curve AUC = 0.957 with RF when photometric information alone is used, and AUC = 0.986 with ERT when photometric and morphological information is used together. When morphological parameters are used, the full width at half maximum is the most important feature. When photometric information is used alone, we observe that broad bands are not necessarily more important than narrow bands, and errors (the width of the distribution) are as important as the measurements (central value of the distribution). In other words, it is apparently important to fully characterize the measurement.
Conclusions. ML algorithms can compete with traditional star and galaxy classifiers; they outperform the latter at fainter magnitudes (r ≳ 21). We use our best classifiers, with and without morphology, in order to produce a value-added catalog.
