Title:	Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data
Authors:	N., Jishnu M. Trott, Cathryn McKinley, Benjamin
Keywords:	Dark ages Reionization First stars –methods Data analysis
Issue Date:	17-Feb-2023
Publisher:	Royal Astronomical Society
Citation:	Monthly Notices of the Royal Astronomical Society, 2023, Vol.520, p6040
Abstract:	Understanding the temporal characteristics of data from low-frequency radio telescopes is of importance in devising suitable calibration strategies. Application of time-series analysis techniques to data from radio telescopes can reveal a wealth of information that can aid in calibration. In this paper , we in vestigate singular spectrum analysis (SSA) as an analysis tool for radio data. We show the intimate connection between SSA and Fourier techniques. We develop the relevant mathematics starting with an idealized periodic dataset and proceeding to include various non-ideal behaviours. We propose a no v el technique to obtain long-term gain changes in data, leveraging the periodicity arising from sky drift through the antenna beams. We also simulate several plausible scenarios and apply the techniques to a 30-day time series data collected during 2021 June from SITARA –a short-spacing two element interferometer for global 21-cm detection. Applying the techniques to real data, we find that the first reconstructed component –the trend –has a strong anti-correlation with the local temperature suggesting temperature fluctuations as the most likely origin for the observed variations in the data. We also study the limitations of the calibration in the presence of diurnal gain variations and find that such variations are the likely impediment to calibrating SITARA data with SSA.
Description:	Open Access
URI:	http://hdl.handle.net/2289/8214
ISSN:	0035-8711 (print) 1365-2966 (online)
Alternative Location:	https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.6040T/abstract https://arxiv.org/abs/2302.07474 https://doi.org/10.1093/mnras/stad522
Copyright:	2023, The Author(s)
Appears in Collections:	Research Papers (A&A)

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