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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/8214
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dc.contributor.authorN., Jishnu-
dc.contributor.authorM. Trott, Cathryn-
dc.contributor.authorMcKinley, Benjamin-
dc.date.accessioned2024-01-30T06:39:09Z-
dc.date.available2024-01-30T06:39:09Z-
dc.date.issued2023-02-17-
dc.identifier.citationMonthly Notices of the Royal Astronomical Society, 2023, Vol.520, p6040en_US
dc.identifier.issn0035-8711 (print)-
dc.identifier.issn1365-2966 (online)-
dc.identifier.urihttp://hdl.handle.net/2289/8214-
dc.descriptionOpen Accessen_US
dc.description.abstractUnderstanding 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.en_US
dc.language.isoenen_US
dc.publisherRoyal Astronomical Societyen_US
dc.relation.urihttps://ui.adsabs.harvard.edu/abs/2023MNRAS.520.6040T/abstracten_US
dc.relation.urihttps://arxiv.org/abs/2302.07474en_US
dc.relation.urihttps://doi.org/10.1093/mnras/stad522en_US
dc.rights2023, The Author(s)en_US
dc.subjectDark agesen_US
dc.subjectReionizationen_US
dc.subjectFirst stars –methodsen_US
dc.subjectData analysisen_US
dc.titleSingular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA dataen_US
dc.typeArticleen_US
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