利用UMLAUT识别大型光谱测量中的单谱线：一种基于无偏拓扑的无监督机器学习算法
Identification of single spectral lines in large spectroscopic surveys using UMLAUT: an Unsupervised Machine Learning Algorithm based on Unbiased Topology
摘要：
当多个光谱特征在同一光谱中清晰可见时，发射线的识别是明确的。然而，在许多情况下，只检测到一条谱线，这使得很难正确地确定红移。我们开发了一种基于无偏拓扑（UMLAUT）的免费无监督机器学习算法，可用于各种各样的环境，包括识别单发射线。为此，该算法结合了不同的信息来源，如发射源的视星等、大小和颜色，以及检测到的线的等效宽度和波长。在每种特定情况下，算法自动识别最相关的（即，能够最小化与输出参数相关联的色散的那些）。输出可以很容易地集成到不同的算法中，使我们能够结合监督和无监督技术，并提高整体准确性。我们在WISP（WFC3 IR Spectroscopic Parallel）调查数据上测试了我们的软件。WISP代表了一个最接近现有的类似近红外光谱调查，将由未来的欧几里得和罗马的任务。这些飞行任务将调查宇宙的大尺度结构，方法是用近红外无狭缝光谱法测量河外天空的一大部分，探测单个发射谱线的相关部分。在我们的测试中，UMLAUT在83.2%的情况下正确识别了真实的线条。当将我们的无监督方法与我们以前开发的监督方法相结合时，准确率略高（84.4%）。
英文摘要：
The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines. To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line. In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter). The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy. We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions. These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines. In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed.
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