Probabilistic Detection of Spectral Line Components
Probabilistic Detection of Spectral Line Components
复制标题
谱线分量的概率检测
DOI：
10.3847/2041-8213/ab8018
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
P. Caselli
中科院分区：
文献类型：
--
作者：
Vlas Sokolov;J. Pineda;J. Buchner;P. Caselli
分辨的运动学信息，例如来自恒星形成区域的分子气体，是从谱线观测中获得的。然而，这些观察结果往往包含多个视线成分，使得估计值更难获得和解释。我们提出了一种全自动的方法，通过选择贝叶斯模型来确定视线上的分量数量，或光谱多样性。基于嵌套采样和传统光谱线建模的底层开源框架使用绿岸氨调查(GAS)获得的英仙座分子云中NGC 1333的大面积氨地图进行了测试。与经典方法相比，该方法在更大的范围内约束了速度和速度弥散。此外，我们发现，与气体数据的单拟合组分分析相比，多个组分之间的速度弥散分布没有实质性变化。这些结果显示了拟合和模型选择方法的能力和相对易用性，使其成为从复杂光谱数据中提取最大信息的独特工具。
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 the 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.
影响因子：
4.8
作者：
Henshaw, J. D.;Longmore, S. N.;Zhang, Q.
通讯作者：
Zhang, Q.