J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology

@ARTICLE{2022A&A...666A.147M,
      author = {{Mas-Buitrago}, P. and {Solano}, E. and {Gonz{\'a}lez-Marcos}, A. and {Rodrigo}, C. and {Mart{\'\i}n}, E.~L. and {Caballero}, J.~A. and {Jim{\'e}nez-Esteban}, F. and {Cruz}, P. and {Ederoclite}, A. and {Ordieres-Mer{\'e}}, J. and {Bello-Garc{\'\i}a}, A. and {Dupke}, R.~A. and {Cenarro}, A.~J. and {Crist{\'o}bal-Hornillos}, D. and {Hern{\'a}ndez-Monteagudo}, C. and {L{\'o}pez-Sanjuan}, C. and {Mar{\'\i}n-Franch}, A. and {Moles}, M. and {Varela}, J. and {V{\'a}zquez Rami{\'o}}, H. and {Alcaniz}, J. and {Sodr{\'e}}, L. and {Angulo}, R.~E.},
        title = "{J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools. II. Second data release and machine learning methodology}",
      journal = {\aap},
    keywords = {methods: data analysis, surveys, virtual observatory tools, stars: low-mass, brown dwarfs, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
        year = 2022,
        month = oct,
      volume = {666},
          eid = {A147},
        pages = {A147},
          doi = {10.1051/0004-6361/202243895},
archivePrefix = {arXiv},
      eprint = {2208.09377},
primaryClass = {astro-ph.SR},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2022A&A...666A.147M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

In this work, published in Astronomy & Astrophysics, we use data from the J-PLUS archive to consolidate a search methodology for ultracool dwarfs in large multi-filter photometric surveys, which will be key for the identification of ultracool dwarfs in upcoming surveys such as J-PAS and Euclid. We also develop a Python algorithm capable of detecting excess in the J-PLUS filters corresponding to the Halpha and CaII H and K emission lines, revealing the potential of multi-filter photometric surveys to detect flaring M dwarfs.

Distance vs. effective temperature diagram for previously reported (blue) and new (yellow) candidate UCDs with good parallax conditions. The vertical dashed line indicates the lower limit of effective temperature for M-type dwarfs (2359 K) according to Pecaut & Mamajek (2013).

Abstract

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 Halpha 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 x 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.