利用径向速度研究双星系统的统计方法
Differential velocity cumulative distribution (DVCD): an efficient statistical method for binary-star analysis in large stellar catalogues using radial velocities
AI总结：
本研究提出DVCD算法，用于通过径向速度数据分析双星系统的统计特性，发现双星比例与表面重力及金属量相关，展示了该方法在大规模分析中的有效性。
摘要：
双星是天体物理学的基础，为恒星演化、银河动力学和基本物理提供关键见解。然而，轨道参数的高维性和观测约束在统计上表征其性质存在显著挑战。在本研究中，我们提出并实现了一种新的算法，即微分速度累积分布（DVCD），用于分析双星系统利用径向速度数据。DVCD方法在准确性及计算效率上优于现有方法，在同等条件下将计算时间减少10^-4至10^-5倍。我们应用DVCD算法到APOGEE DR16的红巨星样本，根据log g和M/H将数据集分为16个子集。我们的发现表明，双星比例随着表面重力降低和金属量增加而减少，为双星的演化过程提供了有价值的约束。本研究强调了DVCD方法在大规模双星统计分析中的潜力。
英文摘要：
Binary stars are fundamental to astrophysics, providing critical insights into stellar evolution, galactic dynamics, and fundamental physics. However, the high dimensionality of orbital parameters and observational constraints present significant challenges in statistically characterizing their properties. In this study, we propose and implement a novel algorithm, the Differential Velocity Cumulative Distribution (DVCD), to analyze binary star systems using radial velocity data. The DVCD method demonstrates superior accuracy and computational efficiency compared to existing approaches, reducing computation time by factors of $10^{-4}$ to $10^{-5}$ under comparable conditions. We applied the DVCD algorithm to red giant samples from APOGEE DR16, dividing the dataset into 16 subsets based on $\log g$ and M/H. Our findings reveal that the binary fraction decreases with decreasing surface gravity and increasing metallicity, offering valuable constraints on the evolutionary processes of binary stars. This study underscores the potential of the DVCD method for large-scale statistical analyzes of binary systems.
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