A method for statistical research on binary stars using radial velocities

Abstract

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.

Figures (21)

• Figure 1: Result of the prediction of orbital parameters using the MCMC approach. The black dashed lines in each 1d subgraph respectively indicate the position of the 15th, 50th, and 85th percentiles.
• Figure 2: Result of the prediction of orbital parameters using the MCMC approach. The black dashed lines in each 1d subgraph respectively indicate the position of the 15th, 50th, and 85th percentiles.
• Figure 3: CDC of $DtajrvList$ of binary star samples with different epochs.
• Figure 4: CDC of $DtajrvList$ of binary star samples with different $\varepsilon$.
• Figure 5: CDC of $DtajrvList$ of single star samples with different $\varepsilon$.