Issue |
A&A
Volume 666, October 2022
|
|
---|---|---|
Article Number | A147 | |
Number of page(s) | 16 | |
Section | Catalogs and data | |
DOI | https://doi.org/10.1051/0004-6361/202243895 | |
Published online | 19 October 2022 |
J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools
II. Second data release and machine learning methodology
1
Centro de Astrobiología (CAB), CSIC-INTA,
Camino Bajo del Castillo s/n,
28692
Villanueva de la Canada, Madrid, Spain
e-mail: pmas@cab.inta-csic.es
2
Spanish Virtual Observatory,
Spain
3
Departamento de Ingenieria Mecánica, Universidad de la Rioja,
San José de Calasanz 31,
26004
Logroño, La Rioja, Spain
4
Instituto de Astrofísica de Canarias (IAC),
Calle Vía Láctea s/n,
38200
La Laguna, Tenerife, Spain
5
Departamento de Astrofísica, Universidad de La Laguna (ULL),
38206
La Laguna, Tenerife, Spain
6
Consejo Superior de Investigaciones Científicas,
28006
Madrid, Spain
7
Centro de Estudios de Física del Cosmos de Aragón (CEFCA),
Plaza San Juan 1,
44001
Teruel, Spain
8
Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Universidad Politécnica de Madrid,
c/ José Gutiérrez Abascal 2,
28006
Madrid, Spain
9
Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo,
Pedro Puig Adam, Sede Departamental Oeste, Modulo 7, 1 a planta,
33203
Gijón, Spain
10
Observatório Nacional - MCTI (ON),
Rua Gal, José Cristino 77, Sào Cristóvão,
20921-400
Rio de Janeiro, Brazil
11
University of Michigan, Department of Astronomy,
1085 South University Ave.,
Ann Arbor, MI, USA
12
University of Alabama, Department of Physics and Astronomy,
Gallalee Hall,
Tuscaloosa, AL
35401, USA
13
Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Unidad Asociada al CSIC,
Plaza San Juan 1,
44001
Teruel, Spain
14
Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo,
05508-090
São Paulo, Brazil
15
Donostia International Physics Center (DIPC),
Paseo Manuel de Lardizabal, 4,
20018
Donostia-San Sebastián, Guipuzkoa, Spain
16
IKERBASQUE, Basque Foundation for Science,
48013
Bilbao, Spain
Received:
28
April
2022
Accepted:
14
July
2022
Context. Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar population and brown dwarfs, from M7 V to cooler objects with L, T, and Y spectral types. Most of them have been discovered using wide-field imaging surveys, for which the Virtual Observatory (VO) has proven to be of great utility.
Aims. We aim to perform a search for UCDs in the entire Javalambre Photometric Local Universe Survey (J-PLUS) second data release (2176 deg2) following a VO methodology. We also explore the ability to reproduce this search with a purely machine learning (ML)-based methodology that relies solely on J-PLUS photometry.
Methods. We followed three different approaches based on parallaxes, proper motions, and colours, respectively, using the VOSA tool to estimate the effective temperatures and complement J-PLUS photometry with other catalogues in the optical and infrared. For the ML methodology, we built a two-step method based on principal component analysis and support vector machine algorithms.
Results. We identified a total of 7827 new candidate UCDs, which represents an increase of about 135% in the number of UCDs reported in the sky coverage of the J-PLUS second data release. Among the candidate UCDs, we found 122 possible unresolved binary systems, 78 wide multiple systems, and 48 objects with a high Bayesian probability of belonging to a young association. We also identified four objects with strong excess in the filter corresponding to the Ca ii H and K emission lines and four other objects with excess emission in the Hα filter. Follow-up spectroscopic observations of two of them indicate they are normal late-M dwarfs. With the ML approach, we obtained a recall score of 92% and 91% in the 20 × 20 deg2 regions used for testing and blind testing, respectively.
Conclusions. We consolidated the proposed search methodology for UCDs, which will be used in deeper and larger upcoming surveys such as J-PAS and Euclid. We concluded that the ML methodology is more efficient in the sense that it allows for a larger number of true negatives to be discarded prior to analysis with VOSA, although it is more photometrically restrictive.
Key words: methods: data analysis / surveys / virtual observatory tools / stars: low-mass / brown dwarfs
© P. Mas-Buitrago et al. 2022
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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