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• W4381161836 abstract "Abstract The volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However, less attention has been given to the production of standardized data sets for assessing the performance of different machine learning algorithms within astronomy and astrophysics. Here we describe in detail the MiraBest data set, a publicly available batched data set of 1256 radio-loud AGN from NVSS and FIRST, filtered to 0.03 &lt; z &lt; 0.1, manually labelled by Miraghaei and Best according to the Fanaroff–Riley morphological classification, created for machine learning applications and compatible for use with standard deep learning libraries. We outline the principles underlying the construction of the data set, the sample selection and pre-processing methodology, data set structure and composition, as well as a comparison of MiraBest to other data sets used in the literature. Existing applications that utilize the MiraBest data set are reviewed, and an extended data set of 2100 sources is created by cross-matching MiraBest with other catalogues of radio-loud AGN that have been used more widely in the literature for machine learning applications." @default.
• W4381161836 created "2023-06-20" @default.
• W4381161836 creator A5014186722 @default.
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• W4381161836 date "2023-01-01" @default.
• W4381161836 modified "2023-10-05" @default.
• W4381161836 title "MiraBest: a data set of morphologically classified radio galaxies for machine learning" @default.
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• W4381161836 doi "https://doi.org/10.1093/rasti/rzad017" @default.
• W4381161836 hasPublicationYear "2023" @default.
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