[论文解读] Gaia Data Release 3: The second Gaia catalogue of Long-Period Variable candidates

T. Lebzelter, N. Mowlavi+10 more|arXiv (Cornell University)|Jun 12, 2022

Stellar, planetary, and galactic studies被引用 6

一句话总结

本论文基于盖亚数据释放3（Gaia DR3）发布了第二版长周期变星（LPV）候选者星表，识别出1,720,558个LPV候选者，其G波段光变幅度大于0.1 mag，其中包括392,240个具有测光周期的候选者和546,468个C型星候选者。星表的完整度约为80%，污染率低于2%，通过结合G波段光曲线与低分辨率RP光谱，实现了对银河系及本星系群中渐近巨星支（AGB）和后-AGB星的大规模研究。

ABSTRACT

The third Gaia Data Release, covering 34 months of data, includes the second Gaia catalogue of long-period variables (LPVs), with G variability amplitudes larger than 0.1 mag (5-95% quantile range). The paper describes the production and content of this catalogue, and the methods used to compute the published variability parameters and identify C-star candidates. We applied various filtering criteria to minimise contamination by other kinds of variables. The variability parameters, period and amplitude, were derived from model fits to the G-band light curves, wherever possible. C stars were identified using their molecular signature in the low-resolution RP spectra. The catalogue contains 1 720 558 LPV candidates, including 392 240 stars with published periods (ranging from 35 to1000 days) and 546 468 stars classified as C-stars candidates. Comparison with literature data (OGLE and ASAS-SN) leads to an estimated 80% of completeness. The recovery rate is about 90% for the most regular stars (typically Miras) and 60% for semi-regular and irregular ones. At the same time, the number of known LPVs is increased by a large factor with respect to the literature data, especially in crowded regions, and the contamination is estimated to be below two percents. Our C-star classification, based on solid theoretical arguments, is consistent with spectroscopically identified C stars in the literature. Caution must however be taken if the S/N ratio is small, in crowded regions or if the source is reddened by some kind of extinction. The quality and potential of the catalogue are illustrated by presenting and discussing LPVs in the solar neighbourhood, in globular clusters and in galaxies of the Local Group. This is the largest all-sky catalogue of LPVs to date with a photometric depth down to G=20 mag, providing a unique data set for research on late stages of stellar evolution.

研究动机与目标

• 利用盖亚数据释放3（DR3）的测光数据，生成一份全面的全天区长周期变星（LPV）候选者星表。
• 提升对LPV的识别与表征能力，特别是对渐近巨星支（AGB）星和碳星的识别，覆盖多样化的银道面环境。
• 通过与现有巡天（如OGLE和ASAS-SN）对比，评估星表的完整度与污染水平。
• 通过提供大量LPV的周期、振幅与光谱分类信息，支持对恒星演化、质量损失与银河系结构的大规模研究。
• 验证低分辨率RP光谱在识别C型星候选者方面的有效性，特别关注在密集或高度消光区域中的局限性。

提出的方法

• 应用筛选标准以最小化非LPV变星类型（如矮新星、激变星等）的污染，聚焦于G波段光曲线中的长周期变光特征。
• 对具有足够数据覆盖的变星源，拟合其G波段光曲线，以推导其周期与振幅（5%至95%分位数范围）。
• 利用低分辨率RP光谱，基于分子吸收特征（特别是C2与CN带）识别C型星候选者。
• 将星表与外部巡天（如OGLE、ASAS-SN）进行交叉匹配，以估算完整度并验证周期与振幅测量结果。
• 采用统计与测光标准，标记虚假信号，并排除短周期或非LPV变光特征。
• 通过太阳邻近区域、球状星团以及本星系群星系（如LMC、SMC、M31）中的已知LPV群体验证结果。

实验结果

研究问题

• RQ1 与现有巡天相比，盖亚DR3星表在长周期变星方面的完整度与可靠性如何？
• RQ2 盖亚DR3对光变曲线跨度达1000天的LPV，其周期与振幅的推导精度如何？
• RQ3 低分辨率RP光谱在LPV中可靠识别C型星候选者的适用程度如何？
• RQ4 银河系及本星系群星系（如LMC、SMC、M31）中LPV的性质与文献中已知群体相比有何异同？
• RQ5 在密集或高度消光区域中，星表存在哪些局限性？这些局限性如何影响周期与光谱分类的准确性？

主要发现

• 星表共包含1,720,558个LPV候选者，其中392,240个具有测量周期（周期范围为35至约1000天），相较于文献中已知的G波段振幅大于0.1 mag的LPV数量，实现了六倍的增长。
• 完整度估计约为80%，其中规则Mira变星的回收率约为90%，SRV与不规则变星的回收率约为60%。
• 非LPV源的污染率估计低于2%，表明样本具有高度可靠性。
• 通过RP光谱中的分子特征识别出546,468个C型星候选者，其结果与文献中光谱确认的C型星高度一致。
• 测光深度达到G = 20等，使星表能够探测到太阳邻近区域及遥远本星系群系统中的暗弱LPV。
• LMC、SMC、M31与M33中LPV的周期-光度图与已知关系高度一致，验证了星表在距离与演化研究中的实用性。