【24h】

PREDICTED PROPERTIES OF ECLIPSING BINARIES OBSERVABLE BY GAIA

机译:GAIA观察到的消除双链的预期属性

获取原文
获取原文并翻译 | 示例

摘要

Using a population synthesis model we estimate the fraction of stars of different types that are likely to show eclipses, as a function of the period. The population model is based on the bse-code from Hurley et al. (2002) which is a rapid binary evolution code that include all common effects in close binaries such as mass transfer, tidal locking, wind etc. We use this to evolve millions of systems from original distributions in separation, mass and eccentricity, with ages between zero and 12 Gyr, to get a population of systems representative of our Galaxy. The binary data from our model are then analysed in order to, statistically, see how many eclipsing systems we should have. This is done with a simple model where we neglect limb-darkening and other complicating effects. Assuming a random distribution of the inclination angle the probability of an eclipse of a given depth, Δm, can easily be calculated. Adding a reasonable fraction of true single stars, we can finally estimate the fraction of eclipsing binaries in limited areas of the HR-diagram, as a function of the period. A first comparison with observational data from the Hipparcos mission shows quite satisfying agreement, and extrapolation to Gaia should thus be a natural application. We find that Gaia will observe about 500 000 eclipsing binaries, this (surprisingly) small number arises from the fact that many eclipsing systems will not be detected by Gaia.
机译:使用总体合成模型,我们估算了可能显示出月食的不同类型恒星的比例,作为周期的函数。人口模型基于Hurley等人的bse代码。 (2002年),它是一个快速的二进制演化代码,其中包括密闭二进制文件中的所有常见影响,例如传质,潮汐锁定,风等。我们使用它来从原始分布的分离,质量和离心率中演化出数百万个系统,并且年龄介于0和12 Gyr,以获得代表我们银河系的大量系统。然后,对来自我们模型的二进制数据进行分析,以便从统计角度看我们应该拥有多少个日食系统。这是通过一个简单的模型完成的,在该模型中我们忽略了肢体变暗和其他复杂影响。假设倾斜角度是随机分布的,则可以很容易地计算出给定深度的食偏的可能性Δm。通过添加合理比例的真实单星,我们最终可以估算出HR图的有限区域中的日食双星所占的比例,作为周期的函数。与Hipparcos任务的观测数据进行的首次比较显示出令人满意的一致性,因此外推到Gaia应该是一种自然的应用。我们发现盖亚将观测到约500,000个日蚀二进制文件,这(令人惊讶地)数量很少是因为盖亚不会检测到许多日蚀系统这一事实。

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