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For submillimeter spectroscopy with ground-based single-dish telescopes,removing noise contribution from the Earth's atmosphere and the instrument isessential. For this purpose, here we propose a new method based on adata-scientific approach. The key technique is statistical matrix decompositionthat automatically separates the signals of astronomical emission lines fromthe drift noise components in the fast-sampled (1--10 Hz) time-series spectraobtained by a position-switching (PSW) observation. Because the proposed methoddoes not apply subtraction between two sets of noisy data (i.e., on-source andoff-source spectra), it improves the observation sensitivity by a factor of$\sqrt{2}$. It also reduces artificial signals such as baseline ripples on aspectrum, which may also help to improve the effective sensitivity. Wedemonstrate this improvement by using the spectroscopic data of emission linestoward a high-redshift galaxy observed with a 2-mm receiver on the 50-m LargeMillimeter Telescope (LMT). Since the proposed method is carried out offlineand no additional measurements are required, it offers an instant improvementon the spectra reduced so far with the conventional method. It also enablesefficient deep spectroscopy driven by the future 50-m class large submillimetersingle-dish telescopes, where fast PSW observations by mechanical antenna ormirror drive are difficult to achieve.

A data-scientific noise-removal method for efficient submillimeter spectroscopy with single-dish telescopes