谱线成分的概率检测
Probabilistic detection of spectral line components
摘要：
从谱线观测中可以得到解析的运动学信息，如恒星形成区的分子气体。然而，这些观测通常包含多个视线分量，使得估计值更难获得和解释。我们提出了一个全自动的方法，确定沿视线的组件的数量，或光谱多重性，通过贝叶斯模型选择。底层的开源框架，嵌套采样和传统的谱线建模的基础上，进行了测试，使用大面积的NGC 1333英仙座分子云氨地图获得的绿岸氨调查（GAS）。与经典方法相比，所提出的方法在更大的区域内约束速度和速度弥散。此外，我们发现，多个组件之间的速度分散分布并没有改变实质上从一个单一的拟合组件分析的GAS数据。这些结果展示了拟合和模型选择方法的能力和相对容易性，这使其成为从复杂光谱数据中提取最大信息的独特工具。
英文摘要：
Resolved kinematical information, such as from molecular gas in star forming regions, is obtained from spectral line observations. However, these observations often contain multiple line-of-sight components, making estimates harder to obtain and interpret. We present a fully automatic method that determines the number of components along the line of sight, or the spectral multiplicity, through Bayesian model selection. The underlying open-source framework, based on nested sampling and conventional spectral line modeling, is tested using the large area ammonia maps of NGC 1333 in Perseus molecular cloud obtained by the Green Bank Ammonia Survey (GAS). Compared to classic approaches, the presented method constrains velocities and velocity dispersions in a larger area. In addition, we find that the velocity dispersion distribution among multiple components did not change substantially from that of a single fit component analysis of the GAS data. These results showcase the power and relative ease of the fitting and model selection method, which makes it a unique tool to extract maximum information from complex spectral data.
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