A data-scientific noise-removal method for efficient submillimeter spectroscopy with single-dish telescopes
Akio Taniguchi, Yoichi Tamura, Shiro Ikeda, Tatsuya Takekoshi, Ryohei, Kawabe

TL;DR
This paper introduces a data-scientific noise-removal method for submillimeter spectroscopy that enhances sensitivity and reduces artifacts without additional measurements, suitable for large single-dish telescopes.
Contribution
The paper presents a novel statistical matrix decomposition technique that automatically separates astronomical signals from noise in fast-sampled spectra, improving sensitivity and data quality.
Findings
Improves sensitivity by a factor of √2 over traditional methods.
Reduces artificial baseline ripples and artifacts in spectra.
Demonstrated effectiveness on high-redshift galaxy data.
Abstract
For submillimeter spectroscopy with ground-based single-dish telescopes, removing 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…
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