{
  "id": "2208.09377",
  "version": "v1",
  "published": "2022-08-19T14:44:13.000Z",
  "updated": "2022-08-19T14:44:13.000Z",
  "title": "J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology",
  "authors": [
    "P. Mas-Buitrago",
    "E. Solano",
    "A. González-Marcos",
    "C. Rodrigo",
    "E. L. Martín",
    "J. A. Caballero",
    "F. Jiménez-Esteban",
    "P. Cruz",
    "A. Ederoclite",
    "J. Ordieres-Meré",
    "A. Bello-García",
    "R. A. Dupke",
    "A. J. Cenarro",
    "D. Cristóbal-Hornillos",
    "C. Hernández-Monteagudo",
    "C. López-Sanjuan",
    "A. Marín-Franch",
    "M. Moles",
    "J. Varela",
    "H. Vázquez Ramió",
    "J. Alcaniz",
    "L. Sodré Jr.",
    "R. E. Angulo"
  ],
  "comment": "Accepted in A&A",
  "categories": [
    "astro-ph.SR",
    "astro-ph.GA",
    "astro-ph.IM"
  ],
  "abstract": "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. We aim to perform a search for UCDs in the entire Javalambre Photometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$) 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. We followed three different approaches based on parallaxes, proper motions, and colours, respectively, using the VOSA tool to estimate the effective temperatures. For the ML methodology, we built a two-step method based on principal component analysis and support vector machine algorithms. 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 H$\\alpha$ filter. With the ML approach, we obtained a recall score of 92% and 91% in the test and blind test, respectively. 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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-08-19T14:44:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "second data",
      "virtual observatory tools",
      "machine learning methodology",
      "ultracool dwarfs",
      "javalambre photometric local universe survey"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}