For submillimeter spectroscopy with ground-based single-dish telescopes, removing the noise contribution from the Earth’s atmosphere and the instrument is essential. For this purpose, here we propose a new method based on a data-scientific approach. The key technique is statistical matrix decomposition that automatically separates the signals of astronomical emission lines from the drift noise components in the fast-sampled (1–10 Hz) time-series spectra obtained by a position-switching (PSW) observation. Because the proposed method does not apply subtraction between two sets of noisy data (i.e., on-source and off-source spectra), it improves the observation sensitivity by a factor of . It also reduces artificial signals such as baseline ripples on a spectrum, which may also help to improve the effective sensitivity. We demonstrate this improvement by using the spectroscopic data of emission lines toward a high-redshift galaxy observed with a 2 mm receiver on the 50 m Large Millimeter Telescope. Since the proposed method is carried out offline and no additional measurements are required, it offers an instant improvement on the spectra reduced so far with the conventional method. It also enables efficient deep spectroscopy driven by the future 50 m class large submillimeter single-dish telescopes, where fast PSW observations by mechanical antenna or mirror drive are difficult to achieve.

对于使用地基单碟望远镜的亚毫米波光谱，消除来自地球大气层和仪器的噪声贡献是必不可少的。为此，我们在这里提出了一种基于数据科学方法的新方法。关键技术是统计矩阵分解，它自动将天文发射线的信号与通过位置切换 (PSW) 观测获得的快速采样 (1-10 Hz) 时间序列光谱中的漂移噪声分量分离。由于所提出的方法没有在两组噪声数据（即源上和源外光谱）之间应用减法，因此将观测灵敏度提高了一个因子. 它还减少了人工信号，例如频谱上的基线波纹，这也可能有助于提高有效灵敏度。我们通过在 50 m 大毫米望远镜上使用 2 mm 接收器观察到的高红移星系的发射线的光谱数据来证明这种改进。由于所提出的方法是离线执行的，不需要额外的测量，因此它提供了对迄今为止传统方法减少的光谱的即时改进。它还可以实现由未来 50 m 级大型亚毫米单碟望远镜驱动的高效深光谱，在这些望远镜中，难以通过机械天线或反射镜驱动实现快速 PSW 观测。