The southern photometric local universe survey (S-PLUS): Improved SEDs, morphologies, and redshifts with 12 optical filters

The Southern Photometric Local Universe Survey (S-PLUS) is imaging ~9300 deg2 of the celestial sphere in 12 optical bands using a dedicated 0.8mrobotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 μm pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel-1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30° , 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, Hd, G band, Mg b triplet, Hα, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than 1 (Gpc/h)3. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ~336 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.Fil: De Oliveira, C. Mendes. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Ribeiro, T.. Universidade Federal de Sergipe; Brasil. National Optical Astronomy Observatory; Estados UnidosFil: Schoenell, W.. Universidade Federal do Rio Grande do Sul; BrasilFil: Kanaan, A.. Universidade Federal de Santa Catarina; BrasilFil: Overzier, R.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; BrasilFil: Molino, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Sampedro, L.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Coelho, P.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Barbosa, C.E.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Cortesi, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Costa Duarte, M.V.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Herpich, F.R.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal de Santa Catarina; BrasilFil: Hernandez Jimenez, J.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Placco, V.M.. University of Notre Dame; Estados Unidos. JINA Center for the Evolution of the Elements ; Estados UnidosFil: Xavier, H.S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Abramo, L.R.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Saito, R.K.. Universidade Federal de Santa Catarina; BrasilFil: Chies Santos, A.L.. Universidade Federal do Rio Grande do Sul; BrasilFil: Ederoclite, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Centro de Estudios de Física del Cosmo de Aragon; EspañaFil: De Oliveira, R. Lopes. Universidade Federal de Sergipe; Brasil. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; Brasil. University of Maryland; Estados UnidosFil: Goncalves, D.R.. Universidade Federal do Rio de Janeiro; BrasilFil: Akras, S.. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Almeida, L.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal do Rio Grande do Norte; BrasilFil: Almeida Fernandes, F.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Beers, T.C.. University of Notre Dame; Estados Unidos. JINA Center for the Evolution of the Elements ; Estados UnidosFil: Bonatto, C.. Universidade Federal do Rio Grande do Sul; BrasilFil: Bonoli, S.. Centro de Estudios de Física del Cosmo de Aragon; EspañaFil: Cypriano, E.S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Vinicius Lima, E.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Smith Castelli, Analia Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentin

On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2

This paper provides a catalogue of stars, quasars, and galaxies for the Southern Photometric Local Universe Survey Data Release 2 (S-PLUS DR2) in the Stripe 82 region. We show that a 12-band filter system (5 Sloan-like and 7 narrow bands) allows better performance for object classification than the usual analysis based solely on broad bands (regardless of infrared information). Moreover, we show that our classification is robust against missing values. Using spectroscopically confirmed sources retrieved from the Sloan Digital Sky Survey DR16 and DR14Q, we train a random forest classifier with the 12 S-PLUS magnitudes + 4 morphological features. A second random forest classifier is trained with the addition of the W1 (3.4 μm) and W2 (4.6 μm) magnitudes from the Wide-field Infrared Survey Explorer (WISE). Forty-four per cent of our catalogue have WISE counterparts and are provided with classification from both models. We achieve 95.76 per cent (52.47 per cent) of quasar purity, 95.88 per cent (92.24 per cent) of quasar completeness, 99.44 per cent (98.17 per cent) of star purity, 98.22 per cent (78.56 per cent) of star completeness, 98.04 per cent (81.39 per cent) of galaxy purity, and 98.8 per cent (85.37 per cent) of galaxy completeness for the first (second) classifier, for which the metrics were calculated on objects with (without) WISE counterpart. A total of 2926 787 objects that are not in our spectroscopic sample were labelled, obtaining 335 956 quasars, 1347 340 stars, and 1243 391 galaxies. From those, 7.4 per cent, 76.0 per cent, and 58.4 per cent were classified with probabilities above 80 per cent. The catalogue with classification and probabilities for Stripe 82 S-PLUS DR2 is available for download. © 2021 The Author(s).This work has been supported by a PhD fellowship to the lead author from Fundacao de Amparo à Pesquisa do Estado de Sao Paulo (FAPESP), 2019/01312-2. LN also acknowledges the support of Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) -Finance Code 001 and FAPESP through process number 2014/10566-4. LN also thanks the staff of the Astronomy department from the University of Florida, where part of this work was done; Marco Antonio dos Santos and Ulisses Manzo Castello for the technical support; Luis Manrique for the technical support, feedback, and discussions about Machine Learning; Christian Massao Tsujiguchi Takagi and Vin ' icius Amaral Haga for the feedback on the accessibility of the figures in this paper; Gustavo Oliveira Schwarz for building the database. CMdO acknowledges funding from FAPESP through grants 2009/542028 and 2019/26492-3 and funding from the Brazilian National Research Council (CNPq), through grant 309209/2019-6. NSTH acknowledges FAPESP (grants 2017/25835-9 and 2015/22308-2). CQ acknowledges support from FAPESP (grants 2015/11442-0 and 2019/06766-1). AM acknowledges FAPESP scholarship grant 2018/25671-9. CEB acknowledges FAPESP, grant 2016/12331-0. FA-F acknowledges funding for this work from FAPESP grant 2018/20977-2. LSJ acknowledges support from Brazilian agencies FAPESP (2019/10923-5) and CNPq (304819/201794). AAC acknowledges support from Fundacao de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ; grant E26/203.186/2016) and CNPq (grants 304971/2016-2 and 401669/2016-5). EVL acknowledges funding for this work from CNPq grant 169181/2017-0 and CAPES grant 88887.470064/2019-00. MLB acknowledges FAPESP, grants 2018/09165-6 and 2019/23388-0. KMD acknowledges support from FAPERJ (grant E-26/203.184/2017), CNPq (grant 312702/2017-5) and the Serrapilheira Institute (grant Serra-1709-17357). AAC acknowledges support from FAPERJ (grant E26/203.186/2016), CNPq (grants 304971/2016-2 and 401669/2016-5), from the Universidad de Alicante under contract UATALENTO18-02, and from the State Agency for Research of the Spanish MCIU through the `Center of Excellence Severo Ochoa' award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). ARL acknowledges the financial support from CNPq through the PCI (Programa de Capacitacao Institucional) fellowship. The S-PLUS project, including the T80-South robotic telescope and the S-PLUS scientific survey, was founded as a partnership between FAPESP, the Observatorio Nacional (ON), the Federal University of Sergipe (UFS), and the Federal University of Santa Catarina (UFSC), with important financial and practical contributions from other collaborating institutes in Brazil, Chile (Universidad de La Serena), and Spain (Centro de Estudios de Física del Cosmos de Aragón, CEFCA). We further acknowledge financial support from FAPESP, CNPq, CAPES, FAPERJ, and the Brazilian Innovation Agency (FINEP).Peer reviewe

Assessing the photometric redshift precision of the S-PLUS survey: The Stripe-82 as a test-case

Indexación ScopusIn this paper we present a thorough discussion about the photometric redshift (photo-z) performance of the Southern Photometric Local Universe Survey (S-PLUS). This survey combines a seven narrow +5 broad passband filter system, with a typical photometric-depth of r ∼ 21 AB. For this exercise, we utilize the Data Release 1 (DR1), corresponding to 336 deg2 from the Stripe-82 region. We rely on the BPZ2 code to compute our estimates, using a new library of SED models, which includes additional templates for quiescent galaxies. When compared to a spectroscopic redshift control sample of ∼100 k galaxies, we find a precision of σz <0.8 per cent, <2.0 per cent, or <3.0 per cent for galaxies with magnitudes r < 17, <19, and <21, respectively. A precision of 0.6 per cent is attained for galaxies with the highest Odds values. These estimates have a negligible bias and a fraction of catastrophic outliers inferior to 1 per cent. We identify a redshift window (i.e. 0.26 < z < 0.32) where our estimates double their precision, due to the simultaneous detection of two emission lines in two distinct narrow bands; representing a window opportunity to conduct statistical studies such as luminosity functions. We forecast a total of ∼2 M, ∼16 M and ∼32 M galaxies in the S-PLUS survey with a photo-z precision of σz <1.0 per cent, <2.0 per cent, and <2.5 per cent after observing 8000 deg2. We also derive redshift probability density functions, proving their reliability encoding redshift uncertainties and their potential recovering the n(z) of galaxies at z < 0.4, with an unprecedented precision for a photometric survey in the Southern hemisphere. © 2020 The Author(s)https://academic-oup-com.recursosbiblioteca.unab.cl/mnras/article/499/3/3884/585601

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter (σNMAD) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21. © 2021 Elsevier B.V. All rights reserved.The author acknowledges the financial support given by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (CAPES, grant 88887.470064/2019-00) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, grant 169181/2017-0), during the development of this research. L. S. J. acknowledges support from Brazilian agencies FAPESP (2019/10923-5) and CNPq (304819/2017-4). L. N. acknowledges the financial support provided by the São Paulo Research Foundation (FAPESP), Brazil (grant 2019/01312-2 and 2014/10566-4). M. L. B. acknowledges the financial support provided by the São Paulo Research Foundation (FAPESP), Brazil (grant 2018/09165-6 and 2019/23388-0). C. Q. acknowledges the financial support provided by the São Paulo Research Foundation, Brazil (grant 2015/11442-0 and 2019/06766-1) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil. F. R. H. thanks FAPESP for the financial support through the program 2018/21661-9. J. L. N. C. is grateful for the financial support received from the Southern Office of Aerospace Research and development (SOARD), grants FA9550-18-1-0018 and FA9550-22-1-0037, of the Air Force Office of the Scientific Research International Office of the United States (AFOSR/IO). M. L. L. D. acknowledges the polish NCN, Poland grant number 2019/34/E/ST9/00133. S. A. acknowledges support under the grant 5077 financed by IAASARS/NOA. Y. J-T has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 898633. Y. J-T. also acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709).Peer reviewe