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arXiv 1901.02663astro-ph.SR
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AIMS --利用星震约束确定恒星参数的新工具

AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

B. M. Rendle, G. Buldgen, A. Miglio, D. Reese, A. Noels, G. R. Davies, T. L. Campante, W. J. Chaplin, M. N. Lund, J. S. Kuszlewicz, L. J. A. Scott, R. Scuflaire… 展开作者

B. M. Rendle, G. Buldgen, A. Miglio, D. Reese, A. Noels, G. R. Davies, T. L. Campante, W. J. Chaplin, M. N. Lund, J. S. Kuszlewicz, L. J. A. Scott, R. Scuflaire, W. H. Ball, J. Smetana, B. Nsamba

摘要:

确定恒星性质的一个关键方面是将观测约束与恒星模型的预测进行比较。大规模天文地震推断(AIMS)是一个开放源代码,使用贝叶斯统计和马尔可夫链蒙特卡罗方法来找到一组代表性的模型,重现了一组给定的经典和天文地震约束。这些模型是通过在预先计算的网格上插值获得的,从而提高了计算效率。我们测试AIMS中不同操作模式的准确性与列日恒星演化代码(主序星和红巨星)计算的恒星模型网格,并比较结果从另一个asteroseismic分析管道,PARAM。此外,使用从网格内的模型生成的人工输入(假设模型是正确的),我们专注于考虑不同组合的观测约束(单个模式频率,周期间隔,视差,光球约束,.)时对代码精度的影响。我们的测试表明,使用AIMS合成数据的参数推断精度的绝对限制,以及代码与预期参数不确定性分布的一致性。插值测试突出了AIMS分析性能的基础物理学的重要性,并提供了参数步长上限的警告。所有测试都证明了AIMS作为一种分析工具的灵活性和能力,以及它对当前和未来的小行星地震数据产出进行准确的集合分析的潜力。

英文摘要:

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Liège stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.

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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