用盖亚DR3搜索新的银河沃尔夫-拉叶星

IF 5.8 2区 物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS
Lionel Mulato, Jaroslav Merc, Stéphane Charbonnel, Olivier Garde, Pascal Le Dȗ, Thomas Petit
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引用次数: 0

摘要

上下文。Gaia DR3于2022年6月发布,包括通过机器学习技术用于分类各种类型发射在线物体的低分辨率XP光谱。盖亚扩展发射在线恒星参数器(ESP-ELS)算法确定了565颗潜在的沃尔夫-拉叶(WR)恒星。其中超过一半已经被称为银河系和麦哲伦星云中的WR星。我们利用盖亚DR3的数据来识别新的银河系WR恒星。我们从盖亚星表中提取了所有通过ESP-ELS算法分类为WC-型或wn -型的恒星。通过应用明智的2MASS颜色选择标准,利用盖亚Hα测量值,并过滤掉已经在各种数据库中编目的对象,我们选择了37个明亮的候选者(G≤16等)和22个暗淡的候选者(G > 16等)。对这些候选者的后续光谱观测使用了智利和法国的2SPOT设备,以及位于卡伦天文台的1米C2PU的Epsilon望远镜。本文主要研究较亮的样品。在这37个目标中,我们分别确认了17颗和16颗新的银河系WC型和wn型WR星。其中三颗最近在一项独立研究中被报道为新的WR恒星。盖亚任务为识别早期调查中遗漏的WR恒星提供了宝贵的资源。虽然这项工作集中在ESP-ELS算法提供的相对较小的起始样本上,但我们的发现强调了改进选择标准以识别算法输出中未包含的其他候选对象的潜力。此外,观测计划强调了小型望远镜在获取G ~ 16等源的初始光谱数据方面的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Search for new Galactic Wolf–Rayet stars using Gaia DR3
Context. Gaia DR3, released in June 2022, included low-resolution XP spectra that have been used for the classification of various types of emission-line objects through machine-learning techniques. The Gaia Extended Stellar Parametrizer for Emission-Line Stars (ESP-ELS) algorithm identified 565 sources as potential Wolf-Rayet (WR) stars. Over half of them were already known as WR stars in the Milky Way and Magellanic Clouds.Aims. We utilized Gaia DR3 data to identify new Galactic WR stars.Methods. We extracted all sources from the Gaia catalog classified as WC- or WN-type stars by the ESP-ELS algorithm. By applying judicious 2MASS color selection criteria, leveraging Gaia Hα measurements, and filtering out objects already cataloged in various databases, we selected 37 bright candidates (G ≤ 16 mag) and 22 faint candidates (G > 16 mag). Spectroscopic follow-up observations of these candidates were conducted using the 2SPOT facilities in Chile and France, as well as the 1 m C2PU’s Epsilon telescope at the Calern Observatory.Results. This paper focuses on the brighter sample. Among the 37 targets, we confirmed 17 and 16 new Galactic WC- and WN-type WR stars, respectively. Three of them were recently reported as new WR stars in an independent study.Conclusions. The Gaia mission provides a valuable resource for identifying WR stars missed in earlier surveys. While this work concentrated on a relatively small starting sample provided by the ESP-ELS algorithm, our findings highlight the potential for refining selection criteria to identify additional candidates not included in the outputs of the algorithm. Furthermore, the observation program underscores the utility of small telescopes in acquiring initial spectral data for sources with magnitudes up to G ~ 16 mag.
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来源期刊
Astronomy & Astrophysics
Astronomy & Astrophysics 地学天文-天文与天体物理
CiteScore
10.20
自引率
27.70%
发文量
2105
审稿时长
1-2 weeks
期刊介绍: Astronomy & Astrophysics is an international Journal that publishes papers on all aspects of astronomy and astrophysics (theoretical, observational, and instrumental) independently of the techniques used to obtain the results.
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