Year: 2020
Type: article
Abstract: 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 deg2of the AEGIS field with 56 narrow-band filters and 4ugribroad-band filters. The miniJPAS primary catalog contains approximately 64 000 objects in therdetection 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 therdetection 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.
Source: Astronomy and Astrophysics
Authors: P. O. Baqui, V. Marra, L. Casarini, R. Angulo, L. A. Díaz-García, C. Hernández-Monteagudo, P. A. A. Lopes, C. López-Sanjuan, D. Muniesa, V. M. Placco, M. Quartin, C. Queiroz, D. Sobral, E. Solano, E. Tempel, J. Varela, J. M. Vílchez, R. Abramo, J. Alcaniz, N. Benitez, S. Bonoli, S. Carneiro, A. J. Cenarro, D. Cristóbal-Hornillos, A. L. de Amorim, C. M. de Oliveira, R. Dupke, A. Ederoclite, R. M. González Delgado, A. Marín-Franch, M. Moles, H. Vázquez Ramió, L. Sodré, K. Taylor
Institutions: Universidade Federal do Espírito Santo, Trieste Astronomical Observatory, Institute for Fundamental Physics of the Universe, Universidade Federal de Sergipe, Ikerbasque, Donostia International Physics Center, Institute of Astronomy and Astrophysics, Academia Sinica, Academia Sinica, Universidad de La Laguna, Centro de Estudios de Física del Cosmos de Aragón, Instituto de Astrofísica de Canarias, Universidade Federal do Rio de Janeiro, Valongo Observatory, NSF NOIRLab, Universidade de São Paulo, Lancaster University, Centro de Astrobiología, Tartu Observatory, University of Tartu, Instituto de Astrofísica de Andalucía, National Observatory, Universidade Federal da Bahia, Universidade Federal de Santa Catarina, University of Alabama, University of Michigan
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, National Aeronautics and Space Administration, Princeton University, Ministerio de Ciencia, Innovación y Universidades, European Commission, Ministry of Education, Culture, Sports, Science and Technology, Fundação de Amparo à Pesquisa do Estado de São Paulo, Academia Sinica, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Cabinet Office, Government of Japan, Ministerio de Economía y Competitividad, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Ministry of Science and Technology, Taiwan, University of Tokyo, Fundação de Amparo à Pesquisa e Inovação do Espírito Santo, National Astronomical Observatory of Japan, High Energy Accelerator Research Organization, Instituto de Astrofísica de Andalucía, Toray Science Foundation, Japan Society for the Promotion of Science, Japan Science and Technology Agency, Agencia Estatal de Investigación
Awards: G1231188857, G1459069146, G1515373893, G1551047073, The 3-D universe: astrophysics with large galaxy surveys, G1707138195, G1735320636, G1948857758, G1951747788, G2153927466, G2350454921, G2421061772, G2921844034, G29316538, The DEEP Survey of the Distant Universe, G3243234535, Collaborative Research: The DEEP Survey--Emergence of the Modern Universe, G3975875632, G407434266, G434337789, G4856116368, G5189296220, G5271735033, G5555131222, Shapes and Alignments of Dark Matter and Baryons in Galaxy Clusters, G5843175489, G6282059440, G6324157175, G6728930308, G6880300208, G6888430614, G6890900265, G7047877648, 花成誘導期におけるメバロン酸代謝系の研究, Collaborative Research: The DEEP Survey--Emergence of the Modern Universe, STAR FORMATION AND GALAXY DYNAMICS AT Z-1: SIRTF, GALEX, & THE DEEP2 GALAXY REDSHIFT SURVEY, G8684520763, Cosmology Beyond Homogeneity and Isotropy, G8909632789, G939699906, G939887807, G98784879
OpenAlex ID: W3043645409