Catalogue of new Herbig Ae/Be and classical Be stars. A machine learning approach to Gaia DR2
| Autor: | D. Baines, I. Mendigutía, Nami Mowlavi, M. Vioque, Ricardo Pérez-Martínez, Rene D. Oudmaijer, M. Schreiner |
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| Přispěvatelé: | European Union's Horizon 2020 research and innovation programme under MSCA ITN-EID grant, 'Talento' Fellowship (Government of Comunidad Autonoma de Madrid), National Aeronautics and Space Administration (NASA), National Science Foundation (NSF), Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737, European Research Council (ERC), Comunidad de Madrid |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Similarity (geometry)
FOS: Physical sciences Context (language use) Astrophysics Machine learning computer.software_genre 01 natural sciences Completeness (order theory) pre-main sequence [Stars] 0103 physical sciences 010303 astronomy & astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Solar and Stellar Astrophysics (astro-ph.SR) formation [Stars] Physics Sequence emission-line [Stars] 010308 nuclear & particles physics business.industry variables T Tauri [Stars] Astronomy and Astrophysics Observable Giant star Astrophysics - Astrophysics of Galaxies Stars Herbig Ae Astrophysics - Solar and Stellar Astrophysics Space and Planetary Science Astrophysics of Galaxies (astro-ph.GA) C-M diagrams Hertzsprung-Russell Artificial intelligence Catalogs Parallax business Astrophysics - Instrumentation and Methods for Astrophysics computer |
| Zdroj: | DIGITAL.INTA Repositorio Digital del Instituto Nacional de Técnica Aeroespacial Instituto Nacional de Técnica Aeroespacial (INTA) instname |
| ISSN: | 0004-6361 |
| Popis: | The intermediate-mass pre-main sequence Herbig Ae/Be stars are key to understanding the differences in formation mechanisms between low- and high-mass stars. The study of the general properties of these objects is hampered by the fact that few and mostly serendipitously discovered sources are known. Our goal is to identify new Herbig Ae/Be candidates to create a homogeneous and well defined catalogue of these objects. We have applied machine learning techniques to 4,150,983 sources with data from Gaia DR2, 2MASS, WISE, and IPHAS or VPHAS+. Several observables were chosen to identify new Herbig Ae/Be candidates based on our current knowledge of this class, which is characterised by infrared excesses, photometric variabilities, and H$\alpha$ emission lines. Classical techniques are not efficient for identifying new Herbig Ae/Be stars mainly because of their similarity with classical Be stars, with which they share many characteristics. By focusing on disentangling these two types of objects, our algorithm has also identified new classical Be stars. We have obtained a large catalogue of 8470 new pre-main sequence candidates and another catalogue of 693 new classical Be candidates with a completeness of $78.8\pm1.4\%$ and $85.5\pm1.2\%$, respectively. Of the catalogue of pre-main sequence candidates, at least 1361 sources are potentially new Herbig Ae/Be candidates according to their position in the Hertzsprung-Russell diagram. In this study we present the methodology used, evaluate the quality of the catalogues, and perform an analysis of their flaws and biases. For this assessment, we make use of observables that have not been accounted for by the algorithm and hence are selection-independent, such as coordinates and parallax based distances. The catalogue of new Herbig Ae/Be stars that we present here increases the number of known objects of the class by an order of magnitude. Comment: Accepted for publication in Astronomy & Astrophysics. Received 14 February 2020 / Accepted 27 April 2020 (18 pages, 9 figures) |
| Databáze: | OpenAIRE |
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