Subspace Techniques for Radio-Astronomical Data Enhancement

TL;DR

This paper introduces subspace decomposition techniques to enhance radio-astronomical data quality by separating signal and noise, allowing for better detection of faint signals and calibration errors beyond traditional averaging methods.

Contribution

It proposes a novel subspace-based approach for data enhancement that accounts for sky variability and instrumental errors, improving over traditional averaging.

Findings

Enhanced signal-to-noise ratio through subspace separation

Detection of faint artifacts and calibration errors

Improved data quality without ignoring subtle variations

Abstract

Radio astronomical observations have very poor signal to noise ratios, unlike in other disciplines. On the other hand, it is possible to observe the object of interest for long time intervals as well as using a wider bandwidth. Traditionally, by averaging in time and in frequency, it has been possible to improve the signal to noise ratio of astronomical observations to improve the dynamic range. This is possible due to the inherent assumption that the object of interest in the sky is invariant over time and the frequency range of observation. However, in reality this assumption does not hold, due to intrinsic variation of the sky as well as due to errors generated by the instrument. In this paper, we shall discuss an alternative to averaging of images, without ignoring subtle changes in the observed data over time and frequency, using subspace decomposition. By separation of data to signal and noise subspaces, not only would this improve the quality of the data, but also enable us to detect faint artifacts due to calibration errors, interference etc.