• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>PASJ: Publications of the Astronomical Society of Japan >Searching for hot subdwarf stars from the LAMOST Spectra. II. Pure spectroscopic identification method for hot subdwarfs
【24h】

Searching for hot subdwarf stars from the LAMOST Spectra. II. Pure spectroscopic identification method for hot subdwarfs

机译：搜索拉米镜谱的热子发行恒星。 II。 热链纯度识别方法

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Employing a new machine-learning method, named the hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The atmospheric parameters of the stars are obtained by fitting the profiles of hydrogen (H) Balmer lines and helium (He) lines with synthetic spectra calculated from non-local thermodynamic equilibrium (NLTE) model atmospheres. Five He-rich hot subdwarf stars were found in our sample with their log (nHe/nH) > -1, while 51 stars are He-poor sdB, sdO and sdOB stars. We also confirmed the two He sequences of hot subdwarf stars found by Edelmann et al. (2003, A&A, 400, 939) in a T-eff-log(nHe/nH) diagram. The HELM algorithm works directly on the observed spectroscopy and is able to filter out spectral properties without supplementary photometric data. The results presented in this study demonstrate that the HELM algorithm is a reliable method to search for hot subdwarf stars after suitable training is performed, and it can also be used to search for other objects which have obvious features in their spectra or images.
机译：采用新的机器学习方法，命名为分层极端学习机（Helm）算法，我们在大型天空区域多物体光纤光谱望远镜（Lamost）调查的第一个数据释放（DR1）中识别了56个热门Subdwarf星。恒星的大气参数是通过用来自非局部热力学平衡（NLTE）模型气氛计算的合成光谱的氢气（H）巴默线和氦（）线的曲线而获得。我们的样品中有五个HE-RID HOMDWARF Stars，他们的日志（NHE / NH）> -1，而51颗星是他糟糕的SDB，SDO和SDOB星星。我们还确认了Edelmann等人发现的热门武士恒星的两个序列。 （2003，A＆A，400,939）在T-Eff-log（NHE / NH）图中。 HelM算法直接在观察到的光谱工作上工作，并且能够在没有补充光度数据的情况下过滤出光谱性质。本研究中提出的结果表明，在执行合适的训练之后，HelM算法是搜索热子狼恒星的可靠方法，并且还可以用于搜索其光谱或图像中具有明显特征的其他对象。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号