Reddening-Free Q Parameters to Classify B-Type Stars with Emission Lines

Author:

Aidelman Yael12ORCID,Cidale Lydia Sonia12ORCID

Affiliation:

1. Instituto de Astrofísica de La Plata, CCT La Plata, CONICET-UNLP, Paseo del Bosque S/N, La Plata B1900FWA, Argentina

2. Departamento de Espectroscopía, Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata (UNLP), Paseo del Bosque S/N, La Plata B1900FWA, Argentina

Abstract

The emission-line B-type stars constitute a heterogeneous group. Many of these stars show similar optical spectroscopic features and color indices, making it difficult to classify them adequately by means of photometric and spectroscopic techniques. Thus, it is relevant to deal with appropriate classification criteria to avoid as many selection effects as possible. For this purpose, we analyzed different reddening-free Q parameters, taking advantage of the Gaia and 2MASS photometric surveys, for both main sequence and emission-line B-type stars. Along with this work, we provided various criteria to search for normal and emission-line B-type stars, using different color–color, Q–color, and Q–Q diagrams. It was also possible to identify stars in different transition phases (i.e., (Rp−J) vs. (J−Ks) diagrams) and to classify them according to their NIR radiation excesses (i.e., the (Bp−Rp) vs. (H−Ks) diagram). Other diagrams, such as the QJKHK vs. (H−Ks) or QBpJHK vs. (Bp−Ks), were very useful to search for and classify different classes of B-type stars with emission lines. These diagrams highlighted the presence of several stars, classified as CBe, with large color excesses that seemed to be caused by the presence of dust in their envelopes. Therefore, these stars would be misclassified. Three groups of HAeBe stars with different intrinsic dust properties were also distinguished. The amount of intrinsic dust emission in the diverse groups of emission-line stars was well-recognized via the QJHK vs. QBpRpHK diagram. The different selection criteria are very important tools for automated designs of machine learning and optimal search algorithms.

Funder

CONICET

Universidad Nacional de La Plata

European Union’s Framework Programme for Research and Innovation Horizon 2020

Publisher

MDPI AG

Subject

Astronomy and Astrophysics

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