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 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 pa...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2023
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| Online Access: | http://purl.org/au-research/grants/arc/FT180100321 http://hdl.handle.net/20.500.11937/96489 |
| _version_ | 1848766156734726144 |
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| author | Thekkeppattu, Jishnu Trott, Cathryn McKinley, Benjamin |
| author_facet | Thekkeppattu, Jishnu Trott, Cathryn McKinley, Benjamin |
| author_sort | Thekkeppattu, Jishnu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 investigate 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 novel 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. |
| first_indexed | 2025-11-14T11:46:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-96489 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:40Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-964892025-01-14T01:01:50Z Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data Thekkeppattu, Jishnu Trott, Cathryn McKinley, Benjamin 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 investigate 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 novel 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. 2023 Journal Article http://hdl.handle.net/20.500.11937/96489 10.1093/mnras/stad522 http://purl.org/au-research/grants/arc/FT180100321 fulltext |
| spellingShingle | Thekkeppattu, Jishnu Trott, Cathryn McKinley, Benjamin Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title | Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title_full | Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title_fullStr | Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title_full_unstemmed | Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title_short | Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data |
| title_sort | singular spectrum analysis of time series data from low-frequency radiometers, with an application to sitara data |
| url | http://purl.org/au-research/grants/arc/FT180100321 http://hdl.handle.net/20.500.11937/96489 |