arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1509.03483astro-ph.SRastro-ph.EPastro-ph.IM
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三参数定律临边暗化系数的有效、无信息采样

Efficient, uninformative sampling of limb-darkening coefficients for a three-parameter law

David M. Kipping

摘要:

恒星边缘变暗影响了广泛的天文测量。它的建模精度限制了任何相关协变参数的精度,例如凌日行星的半径。随着越来越多的精确观测和天体物理参数的可靠估计的重要性,一个新兴的趋势是自由拟合描述边缘暗化规律的边缘暗化系数(LDCs)的选择,以传播我们的无知的真实强度轮廓。在实践中,由于对物理允许的最不发达国家范围进行采样相对容易,这种方法仅限于双参数边缘变暗定律,例如二次定律。在这里,我们提供了一个高效的方法来采样描述一个更准确的三参数非线性规律的最不发达国家。我们首先推导出分析标准,可以快速测试,如果一组最不发达国家是物理的,虽然天真的抽样与这些标准导致接受率小于1%。然后,我们表明,允许的最不发达国家的轨迹可以转化为一个锥状的体积,从中我们能够绘制均匀的样本。我们发现,均匀地从圆锥区域绘制的样本在97.3%的实现中是物理上有效的,并且包含94.4%的允许参数空间的体积。我们在https://github.com/davidkipping/LDC3上提供了Python和Fortran代码(LDC3)来从这个区域采样(并执行反向计算),其中还包括一个子例程来有效地测试样本是否物理有效。

英文摘要:

Stellar limb-darkening impacts a wide range of astronomical measurements. The accuracy to which it is modelled limits the accuracy in any covariant parameters of interest, such as the radius of a transiting planet. With the ever growing availability of precise observations and the importance of robust estimates of astrophysical parameters, an emerging trend has been to freely fit the limb-darkening coefficients (LDCs) describing a limb-darkening law of choice, in order to propagate our ignorance of the true intensity profile. In practice, this approach has been limited to two-parameter limb-darkening laws, such as the quadratic law, due to the relative ease of sampling the physically allowed range of LDCs. Here, we provide a highly efficient method for sampling LDCs describing a more accurate three-parameter non-linear law. We first derive analytic criteria which can quickly test if a set of LDCs are physical, although naive sampling with these criteria leads to an acceptance rate less than 1%. We then show that the loci of allowed LDCs can be transformed into a cone-like volume, from which we are able to draw uniform samples. We show that samples drawn uniformly from the conal region are physically valid in 97.3% of realizations and encompass 94.4% of the volume of allowed parameter space. We provide Python and Fortran code (LDC3) to sample from this region (and perform the reverse calculation) at https://github.com/davidkipping/LDC3, which also includes a subroutine to efficiently test whether a sample is physically valid or not.

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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