A Gas Emission Setup to Evaluate Wideband Sub-mm Spectrometers

Abstract

A gas-cell-based calibration setup was designed to evaluate the wideband response of sub-millimeter spectrometers like DESHIMA (DEep Spectroscopic High-redshift MApper). The use of low pressure gas emission spectra allowed for accurate calibration of the absolute frequency response, and to test the detectability of faint emission spectra with long integration times. This is important to understand and evaluate systematic errors and noise profiles of sub-millimeter astronomical spectrometers before their telescope campaigns.

The setup consisted of a low pressure (~mbar) gas at room temperature in a high vacuum (<10^-3 mbar) chamber in front of a 77K N₂ background. A double-winged rotating chopper was used for signal modulation of the on- and off-source paths to reduce the low-frequency noise profile. The setup has been able to successfully detect the emission spectra of nitrous oxide at 30 mbar and methanol at 1 mbar in the frequency range of 332 to 377 GHz with the prototype DESHIMA spectrometer. Our models showed that lower pressures should be detectable over similar averaging times. The standing spectrum showed to be too irregular for detecting spectral lines in a single measurement. A second measurement was required to subtract the standing features, which extended the total time required beyond the current system stability.

Detailed analysis into optical resonances has shown the importance of anti-reflective (AR) coatings on the main optical interfaces to improve the detectability of the emission spectra. We adapted sub-wavelength pyramid gratings milled into TOPAS windows to reduce a standing wave in the output spectrum of the gas cell setup. Stability of the setup was shown for observation times of up to ~10³ seconds before environmental
noises became dominant. Extensive stability testing has shown the impact of key components in the setup. A two-stage post-processing algorithm was developed to successfully reduce instabilities in the data by removing linear drifts and by removing the common profile over simultaneous read-out data.