Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data

Understanding the temporal characteristics of data from low frequency radio telescopes is importance in devising suitable calibration strategies. Application time series analysis techniques to can reveal a wealth information that aid calibration. In this paper, we investigate singular spectrum (SSA) as an tool for data. We show intimate connection between SSA and Fourier techniques. develop relevant mathematics starting with idealised periodic dataset proceeding include various non-ideal behaviours. propose novel technique obtain long-term gain changes data, leveraging periodicity arising sky drift through antenna beams. also simulate several plausible scenarios apply 30-day collected during June 2021 SITARA - short-spacing two element interferometer global 21-cm detection. Applying real find first reconstructed component trend has strong anti-correlation local temperature suggesting fluctuations most likely origin observed variations study limitations presence diurnal such are impediment calibrating SSA.