论文标题
使用机器学习的光谱和多波长度观测来对尘土飞扬的恒星源进行光谱识别和分类
Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning
论文作者
论文摘要
我们提出了一种机器学习方法，以使用受监督和无监督的方法和分类点源（主要是进化的恒星）来识别和区分尘土飞扬的恒星资源，并使用在IR天空上收集的光度法和光谱数据进行了分类。光谱数据通常用于识别特定的红外源。但是，我们的目标是确定使用多波长数据能够识别这些来源的能力。因此，我们从Sage-Spec Spitzer Legacy和SMC-Spec Spitzer红外光谱仪（IRS）光谱目录中得出了来自大型和小的麦哲伦云的确认来源的训练集。随后，我们应用了各种学习分类器来区分包括年轻恒星对象（YSOS），富含C的渐近巨型分支（CAGB），O-Rich AGB星（OAGB），红色超级恒星（RSG）和后AGBB的恒星子类别。我们已将这些来源的大约700个计数分类。应该强调的是，尽管利用了我们训练过的有限的光谱数据，但准确性和模型的学习曲线为某些模型提供了出色的结果。因此，支持向量分类器（SVC）是该有限数据集的最准确的分类器。
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected over the IR sky. Spectroscopic data is typically used to identify specific infrared sources. However, our goal is to determine how well these sources can be identified using multiwavelength data. Consequently, we developed a robust training set of spectra of confirmed sources from the Large and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Spec Spitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we applied various learning classifiers to distinguish stellar subcategories comprising young stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGB stars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classified around 700 counts of these sources. It should be highlighted that despite utilizing the limited spectroscopic data we trained, the accuracy and models' learning curve provided outstanding results for some of the models. Therefore, the Support Vector Classifier (SVC) is the most accurate classifier for this limited dataset.
