Proyecto de Investigación: PGC2018-097585-B-C21
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PGC2018-097585-B-C21
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The GALANTE photometric survey of the northern Galactic plane: project description and pipeline
(Oxford Academics: Oxford University Press, 2021-07-02) Maíz Apellániz, J.; Alfaro, E. J.; Barbá, R. H.; Holgado, G.; Vázquez Ramió, H.; Varela, J.; Ederoclite, A.; Lorenzo Gutiérrez, A.; García Lario, P.; García Escudero, H.; García, M.; Coelho, P. R. T.; Centros de Excelencia Severo Ochoa, INSTITUTO DE ASTROFISICA DE ANDALUCIA (IAA), SEV-2017-0709; European Space Agency (ESA); Gobierno de Aragón; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Agencia Estatal de Investigación (AEI); Ministerio de Economía y Competitividad (MINECO); Coelho, P. R. T. [0000-0003-1846-4826]
The GALANTE optical photometric survey is observing the northern Galactic plane and some adjacent regions using seven narrow- and intermediate-filters, covering a total of 1618 deg2. The survey has been designed with multiple exposure times and at least two different air masses per field to maximize its photometric dynamic range, comparable to that of Gaia, and ensure the accuracy of its photometric calibration. The goal is to reach at least 1 per cent accuracy and precision in the seven bands for all stars brighter than AB magnitude 17 while detecting fainter stars with lower values of the signal-to-noise ratio. The main purposes of GALANTE are the identification and study of extinguished O+B+WR stars, the derivation of their extinction characteristics, and the cataloguing of F and G stars in the solar neighbourhood. Its data will be also used for a variety of other stellar studies and to generate a high-resolution continuum-free map of the Hα emission in the Galactic plane. We describe the techniques and the pipeline that are being used to process the data, including the basis of an innovative calibration system based on Gaia DR2 and 2MASS photometry.
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.
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.
