机器学习算法在太赫兹光谱数据处理中的应用
Applications of Machine Learning Algorithms In Processing Terahertz Spectroscopic Data
摘要：
介绍了太赫兹平流层观测站2（STO 2）的数据处理软件和一级、二级产品的分布情况。STO 2是一架球载太赫兹望远镜，它勘测了恒星形成区域和银河平面，并产生了大约300，000条光谱。这些数据在很大程度上类似于单碟射电望远镜通常产生的光谱。然而，一小部分数据包含快速变化的条纹/基线特征和漂移噪声，使用传统的数据简化软件无法对其进行充分校正。为了处理STO 2任务的整个科学数据，我们采用了一种新的方法来找到合适的离源光谱，以减少大幅度条纹和新的算法，包括非对称最小二乘法（ALS），独立分量分析（ICA）和基于密度的空间聚类的应用程序与噪声（DBSCAN）。STO 2数据简化软件有效地将条纹的幅度从几百K降低到10 K，并将幅度基线降低到几K。1级产品通常在[CII]光谱中具有几个K的噪声，在[NII]光谱中具有~1 K的噪声。使用一个regridding算法，我们做了恒星形成区域的光谱图和银河系平面调查使用的算法采用贝塞尔-高斯核。天文学界可通过STO 2数据服务器和DataVerse获得1级和2级产品。公众也可以通过Github访问该软件。详细地址见关于数据分发的文件第4节。
英文摘要：
We present the data reduction software and the distribution of Level 1 and Level 2 products of the Stratospheric Terahertz Observatory 2 (STO2). STO2, a balloon-borne Terahertz telescope, surveyed star-forming regions and the Galactic plane and produced approximately 300,000 spectra. The data are largely similar to spectra typically produced by single-dish radio telescopes. However, a fraction of the data contained rapidly varying fringe/baseline features and drift noise, which could not be adequately corrected using conventional data reduction software. To process the entire science data of the STO2 mission, we have adopted a new method to find proper off-source spectra to reduce large-amplitude fringes and new algorithms including Asymmetric Least Square (ALS), Independent Component Analysis (ICA), and Density-based spatial clustering of applications with noise (DBSCAN). The STO2 data reduction software efficiently reduced the amplitude of fringes from a few hundred to 10 K and resulted in baselines of amplitude down to a few K. The Level 1 products typically have the noise of a few K in [CII] spectra and ~1 K in [NII] spectra. Using a regridding algorithm, we made spectral maps of star-forming regions and the Galactic plane survey using an algorithm employing a Bessel-Gaussian kernel. Level 1 and 2 products are available to the astronomical community through the STO2 data server and the DataVerse. The software is also accessible to the public through Github. The detailed addresses are given in Section 4 of the paper on data distribution.
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