Examinando por Autor "Placco, V. M."

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Acceso Abierto
J-PLUS: photometric calibration of large-area multi-filter surveys with stellar and white dwarf loci
(EDP Sciences, 2019-11-05) López Sanjuan, C.; Varela, J.; Cristóbal Hornillos, D.; Vázquez Ramió, H.; Carrasco, J. M.; Tremblay, P. E.; Whitten, D. D.; Placco, V. M.; Marín Franch, A.; Cenarro, A. J.; Ederoclite, A.; Alfaro, Emilio J.; Coelho, P. R. T.; Civera, T.; Hernández Fuertes, J.; Jiménez Esteban, F. M.; Jiménez Teja, Y.; Maíz Apellániz, J.; Sobral, D.; Vílchez, J. M.; Alcaniz, J. S.; Angulo, R. E.; Dupke, R. A.; Hernández Monteagudo, C.; Mendes de Oliveira, Claudia L.; Moles, M.; Sodré, L.; Agencia Estatal de Investigación (AEI); Ministerio de Economía y Competitividad (MINECO); National Aeronautics and Space Administration (NASA); Carrasco Martínez, J. M. [0000-0002-3029-5853]; Sobral, D. [0000-0001-8823-4845]; Mendes de Oliveira, C. [0000-0002-5267-9065]; Vilchez, J. M. [0000-0001-7299-8373]; Jiménez Esteban, F. M. [0000-0002-6985-9476]; Placco, V. [0000-0003-4479-1265]; López Sanjuan, C. [0000-0002-5743-3160]; 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; Unidad de Excelencia Científica María de Maeztu Instituto de Ciencias del Cosmos (ICCUB), MDM-2014-0369
Aims. We present the photometric calibration of the 12 optical passbands observed by the Javalambre Photometric Local Universe Survey (J-PLUS). Methods. The proposed calibration method has four steps: (i) definition of a high-quality set of calibration stars using Gaia information and available 3D dust maps; (ii) anchoring of the J-PLUS gri passbands to the Pan-STARRS photometric solution, accounting for the variation in the calibration with the position of the sources on the CCD; (iii) homogenization of the photometry in the other nine J-PLUS filters using the dust de-reddened instrumental stellar locus in (𝒳 − r) versus (g − i) colours, where 𝒳 is the filter to calibrate. The zero point variation along the CCD in these filters was estimated with the distance to the stellar locus. Finally, (iv) the absolute colour calibration was obtained with the white dwarf locus. We performed a joint Bayesian modelling of 11 J-PLUS colour–colour diagrams using the theoretical white dwarf locus as reference. This provides the needed offsets to transform instrumental magnitudes to calibrated magnitudes outside the atmosphere. Results. The uncertainty of the J-PLUS photometric calibration, estimated from duplicated objects observed in adjacent pointings and accounting for the absolute colour and flux calibration errors, are ∼19 mmag in u, J0378, and J0395; ∼11 mmag in J0410 and J0430; and ∼8 mmag in g, J0515, r, J0660, i, J0861, and z. Conclusions. We present an optimized calibration method for the large-area multi-filter J-PLUS project, reaching 1–2% accuracy within an area of 1022 square degrees without the need for long observing calibration campaigns or constant atmospheric monitoring. The proposed method will be adapted for the photometric calibration of J-PAS, that will observe several thousand square degrees with 56 narrow optical filters.
Acceso Abierto
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.
Acceso Abierto
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.