The Nature of Blue Stars with Mid-infrared Excesses in the Large Magellanic Cloud

Autor: Ryoko Ishioka, You-Hua Chu, Austin Edmister, Robert A. Gruendl, Lizhong Zhang, Ju Zhu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: The Astrophysical Journal Supplement Series, Vol 265, Iss 1, p 18 (2023)
Druh dokumentu: article
ISSN: 1538-4365
0067-0049
DOI: 10.3847/1538-4365/acad06
Popis: We present low-resolution optical spectra and classifications of 92 blue objects with mid-infrared excesses in the Large Magellanic Cloud. The majority of these objects were selected with the criteria of U − B < 0 and V < 17 from the potential young stellar object (YSO) candidates in Gruendl & Chu (GC09), which were identified based on Spitzer Infrared Array Camera and Multiband Imaging Photometer for Spitzer observations in conjunction with optical photometry from the Magellanic Clouds Photometric Survey. Many of the sample objects have ambiguous classifications. We examined the properties of these 92 objects using low-resolution optical spectra obtained with the SOAR 4.1 m Telescope at Cerro Pachon and the Blanco 4 m Telescope at Cerro Tololo Inter-American Observatory, supplemented by available photometric and imaging observations. We estimated the spectral types, temperatures, and luminosities of these objects from the optical to near-IR spectral energy distributions based on the photometric data, and further examined stellar absorption line features in the optical spectra to verify the spectral types. The interstellar/circumstellar environments, assessed from nebular line imaging observations and nebular lines detected in the stellar spectra, further helped constrain the nature of stars. Among these 92 objects, we confirm 42 stars as YSOs, and the remaining 50 objects as protoplanetary nebulae, post-AGB/RGB stars, blue evolved massive stars, stars with dust in vicinity, or uncertain classifications. Our results show that the photometric criteria in GC09 are generally effective in the initial selection of YSO candidates, and the low-resolution spectroscopy combined with environment assessment can be useful to better constrain the classifications and ameliorate most ambiguities.
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