发表机构
Universidad Católica del Norte; Arizona State University（天主教北大学; 亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LACHESIS通过贝叶斯模型平均整合五个等时线网格，利用嵌套采样从测光和光谱数据推断恒星质量、半径和年龄，明确量化网格选择带来的系统性不确定，实现稳健参数估计。
AI 中文摘要
精确的恒星参数支撑着天体物理学的众多领域，从系外行星系统到银河考古学。对于孤立场星，质量和尤其是年龄通常由恒星演化模型推断得出，然而这些推断依赖于所选择的模型网格，且不同网格之间的系统性差异可与任何单一流程的统计不确定性相抗衡。我们提出LACHESIS，一个Python软件包，它从测光和光谱数据中确定恒星质量、半径和年龄，同时明确考虑来自模型网格选择的系统性不确定性。我们在[Fe/H]、log年龄和等效演化阶段上插值五个独立的等时线网格，并以数据为条件，使用嵌套采样对每个网格进行采样，然后通过贝叶斯模型平均结合各网格的后验分布，以贝叶斯证据为权重对各网格加权。我们针对开普勒星震学矮星和亚巨星的基准数据集验证了LACHESIS。质量恢复的稳健散布约为4%，半径约为2%，半径偏差可忽略（<1%），质量系统偏差约为3%。年龄恢复的稳健散布为30%，且强烈依赖于演化状态；网格间系统性贡献了约9%的下限，这是一个次主导但真实存在的项，而单一网格拟合会遗漏它。模型平均比选择单一网格具有更好的校准性：一旦纳入参考不确定性，其可信区间接近名义覆盖率，并且在注入测试中即使生成模型位于集合之外，其覆盖率也接近名义水平。通过对恒星模型网格集合进行边缘化，LACHESIS将网格选择的系统性直接纳入后验分布，因此报告的不确定性不再仅反映网格内的数据噪声。这为系外行星宿主星和恒星种群研究提供了均匀、特征明确的质量、半径和年龄。
英文摘要
Accurate stellar parameters underpin much of astrophysics, from exoplanetary systems to Galactic archaeology. For isolated field stars, masses and especially ages are usually inferred from stellar evolution models, yet these inferences depend on the chosen model grid, and the systematic differences between grids can rival the statistical uncertainties of any single pipeline. We present LACHESIS, a Python package that determines stellar masses, radii, and ages from photometry and spectroscopy while explicitly accounting for the systematic uncertainty from the choice of model grid. We interpolate five independent isochrone grids in [Fe/H], log age, and equivalent evolutionary phase conditioned on the data, sample each with nested sampling, and combine the per-grid posteriors by Bayesian model averaging, weighting each grid by its Bayesian evidence. We validate LACHESIS against a benchmark of Kepler asteroseismic dwarfs and subgiants. Masses are recovered with a robust scatter of ~4% and radii to ~2%, with negligible radius bias (<1%) and a small ~3% mass systematic. Age is recovered with a robust scatter of 30% that depends strongly on evolutionary state; the grid-to-grid systematic contributes a ~9% floor, a subdominant but real term that a single-grid fit omits. Model averaging is better calibrated than selecting a single grid: its credible intervals approach nominal coverage once the reference uncertainty is included, and cover near the nominal rate in injection tests even when the generating model lies outside the ensemble. By marginalizing over an ensemble of stellar model grids, LACHESIS folds the choice-of-grid systematic directly into the posterior, so the reported uncertainties no longer reflect only within-grid data noise. This delivers homogeneous, well-characterized masses, radii, and ages for exoplanet hosts and stellar population studies.
CommentsAccepted in A&A. 12 pages, 7 figures, 1 page of appendix, abstract changed from accepted version due to character constraints
