论文标题
部分可观测时空混沌系统的无模型预测
Applications of Machine Learning Algorithms In Processing Terahertz Spectroscopic Data
论文作者
论文摘要
我们介绍了平流层terahertz天文台2(STO2)的1级和2级产品的数据还原软件。气球传播的Terahertz望远镜STO2调查了恒星形成区域和银河平面,并产生了大约300,000个光谱。数据在很大程度上与单次射电望远镜通常产生的光谱相似。但是,一部分数据包含迅速变化的边缘/基线特征和漂移噪声,使用常规数据减少软件无法充分纠正。为了处理STO2任务的整个科学数据,我们采用了一种新方法来找到适当的外源光谱,以减少大振幅条纹和新算法,包括不对称的最小平方(ALS),独立的组件分析(ICA),以及基于密度的空间集群应用使用噪声(dbscan)。 STO2数据还原软件有效地将边缘的幅度从几百到10 K降低,并导致幅度下降到几个K。1级产品通常在[CII]光谱中具有几个k的噪声,[nii]光谱中的〜1 k中的噪声。使用重制算法,我们使用贝塞尔 - 高斯内核制作了恒星形成区域的光谱图和银河平面调查。 1级和2级产品可通过STO2数据服务器和数据服务器提供天文学社区。该软件也可以通过GitHub访问公众。有关数据分布的论文第4节中给出了详细的地址。
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.