Foam: A Python package for forward asteroseismic modelling of gravity modes

M. Michielsen
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Abstract

Summary Asteroseismology, the study of stellar pulsations, offers insights into the internal structures and evolution of stars. Analysing the variations in a star’s brightness allows the determination of fundamental properties such as mass, radius, age, and chemical composition. Asteroseismology heavily relies on computational tools, but a significant number of them are closed-source, thus inaccessible to the broader astronomic community. This manuscript presents Foam , a Python package designed to perform forward asteroseismic modelling of stars exhibiting gravity modes. It automates and streamlines a considerable fraction of the modelling process, comparing grids of theoretical stellar models and their oscillation frequencies to observed frequency sets in stars. Foam offers the flexibility to employ diverse modelling approaches, allowing users to choose different methodologies for matching theoretically predicted oscillations to observations. It provides options to utilise various sets of observables for comparison with their theoretical counterparts, employ different merit functions for assessing goodness of fit, and to incorporate nested subgrids in a statistically rigorous manner. For applications of these methodologies in modelling observed gravity modes, refer to Michielsen et al. (2021) and Michielsen et al. (2023).
泡沫用于重力模式前向小行星地震建模的 Python 软件包
摘要 星震学是对恒星脉动的研究,有助于深入了解恒星的内部结构和演化过程。通过分析恒星亮度的变化,可以确定恒星的基本属性,如质量、半径、年龄和化学成分。星震学在很大程度上依赖于计算工具,但其中有相当一部分是闭源的,因此广大天文学界无法使用。本手稿介绍的 Foam 是一个 Python 软件包,旨在对表现出重力模式的恒星进行前向小行星地震建模。它将相当一部分建模过程自动化和简化,将理论恒星模型网格及其振荡频率与观测到的恒星频率集进行比较。Foam 可以灵活地采用不同的建模方法,允许用户选择不同的方法将理论预测的振荡与观测结果相匹配。它提供的选项包括:利用各种观测数据集与其理论对应数据进行比较,采用不同的优点函数来评估拟合度,以及以严格的统计方式纳入嵌套子网格。关于这些方法在模拟观测到的重力模式中的应用,请参阅 Michielsen 等人(2021 年)和 Michielsen 等人(2023 年)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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