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arXiv 2303.09712astro-ph.GAastro-ph.EPastro-ph.IMastro-ph.SR
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Gaia DR3中邻近加速恒星候选体星表

A catalog of nearby accelerating star candidates in Gaia DR3

Marc L. Whiting, Joshua B. Hill, Benjamin C. Bromley, Scott J. Kenyon

AI总结:

本文基于监督机器学习构建了Gaia邻近加速恒星星表GNASC,包含29,684个邻近加速候选体,覆盖Gaia DR3高置信度加速源的96%以上,并扩展了HGCA,展示了机器学习发现邻近恒星隐藏伴星的潜力。

摘要:

我们描述了一个新的加速恒星候选体星表,其选取条件为Gaia G星等≤17.5等且距离d≤100秒差距。该星表被命名为Gaia邻近加速恒星星表(GNASC),包含29,684个成员,这些成员是利用一种监督机器学习算法识别出来的,该算法在依巴谷-Gaia加速星表(HGCA)、Gaia Data Release 2和Gaia Early Data Release 3上进行了训练。我们利用了这两个Gaia星表观测时间线的差异以及关于天体测量建模质量的信息,其前提是加速度将与天体测量不确定度相关。星表成员资格的依据是:能否以高置信度(大于99.9%)排除三十年间的恒定自行。测试数据表明,星表成员中每个成员具有真实天体测量加速的可能性为68%;该星表的子集表现更优,可能性超过85%。我们将GNASC与Gaia Data Release 3及其基于精确天体测量拟合以高置信度检测到加速度的恒星表进行了比较。我们的星表是在没有使用该信息的情况下导出的,却捕获了该表中满足我们选取标准的源的96%以上。此外,GNASC包含了原本不在依巴谷巡天中的明亮、邻近候选体,包括已知双星系统的成员以及伴星尚未被识别的恒星。因此,它扩展了HGCA,并展示了机器学习方法在未来天体测量巡天中发现邻近恒星隐藏伴星的潜力。

英文摘要:

We describe a new catalog of accelerating star candidates with Gaia $G\le 17.5$ mag and distances $d\le 100$ pc. Designated as Gaia Nearby Accelerating Star Catalog (GNASC), it contains 29,684 members identified using a supervised machine-learning algorithm trained on the Hipparcos-Gaia Catalog of Accelerations (HGCA), Gaia Data Release 2, and Gaia Early Data Release 3. We take advantage of the difference in observation timelines of the two Gaia catalogs and information about the quality of the astrometric modeling based on the premise that acceleration will correlate with astrometric uncertainties. Catalog membership is based on whether constant proper motion over three decades can be ruled out at high confidence (greater than 99.9%). Test data suggest that catalog members each have a 68% likelihood of true astrometric acceleration; subsets of the catalog perform even better, with the likelihood exceeding 85%. We compare the GNASC with Gaia Data Release 3 and its table of stars for which acceleration is detected at high confidence based on precise astrometric fits. Our catalog, derived without this information, captured over 96% of sources in the table that meet our selection criteria. In addition, the GNASC contains bright, nearby candidates that were not in the original Hipparcos survey, including members of known binary systems as well as stars with companions yet to be identified. It thus extends the HGCA and demonstrates the potential of the machine-learning approach to discover hidden partners of nearby stars in future astrometric surveys.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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