Data science based efficient and automated spectroscopy for submillimeter single-dish telescopes

In this paper, we present a new data scientific approach for efficient and automated spectroscopy with millimeter and submillimeter single-dish telescopes. The proposed approach avoids direct subtraction between two noisy spectra (i.e. on-source and off-source spectra) that is common in the current data reduction: It then offers us to improve the observation sensitivity by a factor of $\gtrsim\sqrt{2}$ and reduce artificial baseline ripples in parallel with developing observational instruments. We demonstrate such upgrades in the real observed spectra taken by existing large millimeter single-dish telescopes. We finally discuss the application of the proposed approach for the future large submillimeter single-dish telescopes that will yield petabytes of data resulting from sensitive, wide field $(\gtrsim 1\mathrm{deg}^{2})$, and wide band ($\gt100$ GHz) imaging spectroscopy.