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arXiv 2206.03074astro-ph.SRastro-ph.IM
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Gaia DR3白矮星-主序星谱的随机森林分类:可行性研究

Random Forest classification of Gaia DR3 white dwarf-main sequence spectra: a feasibility study

David Echeverry, Santiago Torres, Alberto Rebassa-Mansergas, Aina Ferrer-Burjachs

摘要:

第三次盖亚数据发布提供了大约2亿个源的低分辨率光谱。预计其中相当大的一部分包含一颗白矮星(WD),要么是孤立的,要么是在一个有主序星(MS)伴星的双星系统中,即一颗白矮星-主序星(WDMS)双星。利用一个统一的随机森林算法中使用的分类的WD,我们将其扩展到研究分类盖亚WDMS二进制光谱的可行性。随机森林算法首先使用一组合成光谱进行训练,这些合成光谱是通过将各个WD和MS光谱结合起来生成的,用于整个有效温度和表面重力范围。此外,借助于详细的人口合成程序,我们模拟了上述人口的盖亚光谱。为了评估模型的性能,一组指标应用于我们的分类。我们的研究结果表明,对于分辨率高于~300的分类的准确性完全取决于光谱的SNR,而低于该值的SNR应增加,因为分辨率降低,以保持一定的准确性。然后将该算法应用于已经分类的SDSS WDMS目录,揭示了自动分类具有与以前的分类方法相当(甚至更高)的准确性。最后,我们模拟了{ \it Gaia}光谱,表明我们的算法能够正确分类近80%的合成WDMS光谱。我们的算法是一个有用的工具,在分析和分类的真正的盖亚WDMS光谱。即使是那些光谱占主导地位的流量的MS星,该算法达到了很高的精度(60%)。

英文摘要:

The third Gaia data release provides low-resolution spectra for around 200 million sources. It is expected that a sizeable fraction of them contain a white dwarf (WD), either isolated, or in a binary system with a main-sequence (MS) companion, i.e. a white dwarf-main sequence (WDMS) binary. Taking advantage of a consolidated Random Forest algorithm used in the classification of WDs, we extend it to study the feasibility of classifying Gaia WDMS binary spectra. The Random Forest algorithm is first trained with a set of synthetic spectra generated by combining individual WD and MS spectra for the full range of effective temperatures and surface gravities. Moreover, with the aid of a detailed population synthesis code, we simulate the Gaia spectra for the above mentioned populations. For evaluating the performance of the models, a set of metrics are applied to our classifications. Our results show that for resolving powers above ~300 the accuracy of the classification depends exclusively on the SNR of the spectra, while below that value the SNR should be increased as the resolving power is reduced to maintain a certain accuracy. The algorithm is then applied to the already classified SDSS WDMS catalogue, revealing that the automated classification exhibits an accuracy comparable (or even higher) to previous classification methods. Finally, we simulate the {\it Gaia} spectra, showing that our algorithm is able to correctly classify nearly 80% the synthetic WDMS spectra. Our algorithm represents a useful tool in the analysis and classification of real Gaia WDMS spectra. Even for those spectra dominated by the flux of the MS stars, the algorithm reaches a high degree of accuracy (60%).

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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