通过MCMC解译多周期单谱线双星光谱以揭示星际介质特征
Disentangling Multi-epoch Single-lined Binary Star Spectra to Reveal Interstellar Medium Features via MCMC
AI总结：
该研究提出MCMC-RVSD算法，通过轨道运动分离双星光谱的恒星与星际特征，解决传统模板匹配精度受限问题，可精确测量星际特征，适用于LAMOST巡天数据。
摘要：
弥散星际带（DIBs）是光学和近红外光谱中载体未被确认的星际吸收特征，当在冷星光谱中观测时，会受到恒星谱线的严重污染。传统方法依赖模板匹配去除恒星成分，但其精度受模板质量的根本限制。我们提出一种不依赖模板的算法，利用轨道运动分离双星光谱中的恒星与星际特征：恒星谱线随径向速度（RV）变化而位移，而DIBs在静止参考系中保持静止。该算法为基于径向速度的贝叶斯马尔可夫链蒙特卡罗（MCMC）框架，即MCMC-RVSD算法，可同时拟合两种成分。我们通过将其应用于模拟LAMOST中分辨率观测的 mock 光谱验证了该方法，该方法可精确恢复DIB λ6614的参数，对分辨率R=3000-7500的光谱表现可靠。我们将该方法应用于LAMOST中分辨率巡天的434个真实双星源，并引入MCMC自相关时间（τ_DIB）评估拟合质量。分析显示，径向速度跨度>20 km s⁻¹是谱线成功分离的主要驱动因素，更密集的轨道采样可进一步提升效果，且该算法在信噪比SNR>50、径向速度误差<5 km s⁻¹的典型观测噪声下仍保持稳健。在理想条件下，该方法的参数不确定性显著小于传统模板匹配。此外，通过调整模型和先验设置，MCMC-RVSD算法还可处理多恒星谱线污染、非高斯DIB及其他DIB。该算法为时间域光谱巡天中精确测量星际特征提供了可靠的通用工具。
英文摘要：
Diffuse Interstellar Bands (DIBs) are interstellar absorption features with unidentified carriers in the optical and near-infrared spectra. When observed in cool star spectra, they are heavily contaminated by stellar lines. Traditional methods rely on template matching to remove the stellar component, but their accuracy is fundamentally limited by template quality. We present a template-independent algorithm that separates stellar and interstellar features in binary star spectra by exploiting orbital motion: stellar lines shift with changing radial velocity (RV), whereas DIBs remain stationary in the rest frame. The MCMC-RVSD algorithm, implemented as an RV-based Bayesian Markov Chain Monte Carlo framework, fits both components simultaneously. We validated our approach by applying it to mock spectra simulating LAMOST medium-resolution observations. The method precisely recovers the parameters of DIB $λ$6614. It performs reliably for spectra with $R=3000$-$7500$. We applied the method to 434 real binary sources from the LAMOST medium-resolution survey and introduced the MCMC autocorrelation time ($τ_{\mathrm{DIB}}$) to assess the fit quality. Our analysis shows that an RV span $>20$ km s$^{-1}$ is the primary driver of successful line separation. Denser orbital sampling provides further improvement, and the algorithm remains robust against typical observational noise at SNR $>50$ and RV errors $<5$ km s$^{-1}$. Under ideal conditions, our approach yields significantly smaller parameter uncertainties than traditional template matching. Additionally, the MCMC-RVSD algorithm can also handle multi-stellar line contamination, non-Gaussian DIBs and other DIBs by adjusting model and prior settings. This algorithm provides a reliable, general-purpose tool for precise measurement of interstellar features in time-domain spectroscopic surveys.
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