Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration
Radio interferometry is an observational technique used to study astrophysical phenomena. Data gathered by an interferometer require substantial processing before astronomers can extract scientific information from them. Data processing consists of a sequence of calibration and analysis procedures w...
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IOP Publishing
2024-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/ad88f6 |
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| author | Brian M. Kirk Urvashi Rau Ramyaa Ramyaa |
| author_facet | Brian M. Kirk Urvashi Rau Ramyaa Ramyaa |
| author_sort | Brian M. Kirk |
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| description | Radio interferometry is an observational technique used to study astrophysical phenomena. Data gathered by an interferometer require substantial processing before astronomers can extract scientific information from them. Data processing consists of a sequence of calibration and analysis procedures where choices must be made about the sequence of procedures as well as the specific configuration of the procedure itself. These choices are typically based on a combination of measurable data characteristics, an understanding of the instrument itself, an appreciation of the trade-offs between compute cost and accuracy, and a learned understanding of what is considered best practice. A metric of absolute correctness is not always available and validity is often subject to human judgment. The underlying principles and software configurations to discern a reasonable workflow for a given data set is the subject of training workshops for students and scientists. Our goal is to use objective metrics that quantify best practice, and numerically map out the decision space with respect to our metrics. With these objective metrics we demonstrate an automated, data-driven, decision system that is capable of sequencing the optimal action(s) for processing interferometric data. This paper introduces a simplified description of the principles behind interferometry and the procedures required for data processing. We highlight the issues with current automation approaches and propose our ideas for solving these bottlenecks. A prototype is demonstrated and the results are discussed. |
| format | Article |
| id | doaj-art-338fabd9bb0a4cce80878cf19ab67644 |
| institution | OA Journals |
| issn | 1538-3881 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
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| series | The Astronomical Journal |
| spelling | doaj-art-338fabd9bb0a4cce80878cf19ab676442025-08-20T02:34:44ZengIOP PublishingThe Astronomical Journal1538-38812024-01-0116914310.3847/1538-3881/ad88f6Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in CalibrationBrian M. Kirk0https://orcid.org/0000-0002-7753-9130Urvashi Rau1Ramyaa Ramyaa2New Mexico Institute of Mining and Technology , USA; National Radio Astronomy Observatory , USANational Radio Astronomy Observatory , USANew Mexico Institute of Mining and Technology , USARadio interferometry is an observational technique used to study astrophysical phenomena. Data gathered by an interferometer require substantial processing before astronomers can extract scientific information from them. Data processing consists of a sequence of calibration and analysis procedures where choices must be made about the sequence of procedures as well as the specific configuration of the procedure itself. These choices are typically based on a combination of measurable data characteristics, an understanding of the instrument itself, an appreciation of the trade-offs between compute cost and accuracy, and a learned understanding of what is considered best practice. A metric of absolute correctness is not always available and validity is often subject to human judgment. The underlying principles and software configurations to discern a reasonable workflow for a given data set is the subject of training workshops for students and scientists. Our goal is to use objective metrics that quantify best practice, and numerically map out the decision space with respect to our metrics. With these objective metrics we demonstrate an automated, data-driven, decision system that is capable of sequencing the optimal action(s) for processing interferometric data. This paper introduces a simplified description of the principles behind interferometry and the procedures required for data processing. We highlight the issues with current automation approaches and propose our ideas for solving these bottlenecks. A prototype is demonstrated and the results are discussed.https://doi.org/10.3847/1538-3881/ad88f6Computational methodsComputational astronomyInterdisciplinary astronomyRadio astronomy |
| spellingShingle | Brian M. Kirk Urvashi Rau Ramyaa Ramyaa Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration The Astronomical Journal Computational methods Computational astronomy Interdisciplinary astronomy Radio astronomy |
| title | Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration |
| title_full | Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration |
| title_fullStr | Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration |
| title_full_unstemmed | Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration |
| title_short | Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration |
| title_sort | reinforcement learning for data driven workflows in radio interferometry i principal demonstration in calibration |
| topic | Computational methods Computational astronomy Interdisciplinary astronomy Radio astronomy |
| url | https://doi.org/10.3847/1538-3881/ad88f6 |
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