AI 中文总结
该研究提出基于机器学习化学嵌入的自动算法，用于天文线巡天的分子分配与预测，在两个天体源的验证中高效准确识别大量分子，为天文分子研究提供新方法。
AI 中文摘要
现代射电望远镜产生海量观测数据，为星际源的分子组成提供了宝贵见解。识别这些数据集中的分子通常涉及耗时且费力的手动分析。本文提出一种用于星际线巡天中分子自动分配的方法，该算法分为两个主要阶段：第一阶段自动确定数据的关键参数，包括激发温度、线宽和源速度；第二阶段通过评估分子候选物的光谱匹配度，并分析其与星际源的化学相关性，来分配观测到的谱线峰。化学相关性通过利用基于机器学习的分子嵌入技术分析观测物种占据的化学空间区域来确定。在谱线分配之后，该信息用于生成占据相同化学空间区域的新分子候选物，这些新生成的物种是观测数据中进一步研究的有希望的目标。该算法在暗分子云TMC-1和恒星形成区IRAS 16293-2422B的谱线巡天中得到验证，在两种情况下，均在17分钟或更短时间内识别出至少67种分子，占分析线强度的90%以上，同时保持高水平的准确性。
英文摘要
Modern radio telescopes generate vast amounts of observational data, offering valuable insights into the molecular composition of interstellar sources. Identifying the molecules within these datasets typically involves time-consuming and labor-intensive manual analysis. This paper presents an automated method for assigning molecules in interstellar line surveys. The algorithm operates in two main stages. First, it automatically determines key parameters of the data, including excitation temperature, line width, and source velocity. Next, it assigns the observed spectral peaks by evaluating the spectroscopic match of the molecular candidates along with analyzing their chemical relevance to the interstellar source. The chemical relevance is determined by leveraging machine-learning-based molecular embedding techniques to analyze the regions of chemical space occupied by the observed species. Following the line assignment, this information is then used to generate new molecular candidates that occupy the same regions of chemical space. These newly generated species serve as promising targets for further investigation in the observational data. The algorithm was validated on spectral line surveys of the dark molecular cloud TMC-1 and the star-forming region IRAS 16293-2422B. In both cases, it identified at least 67 molecular species, accounting for over 90 percent of the analyzed line intensity, in 17 minutes or less while maintaining a high level of accuracy.
CommentsAccepted for publication in The Astrophysical Journal
