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dc.contributor.authorBastien, David
dc.contributor.authorOozeer, Nadeem
dc.contributor.authorSomanah, Radhakrishna
dc.identifier.citationBastien, D. et al. 2016. Classifying bent radio galaxies from a mixture of point-like/extended images with machine learning. IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 10-13 Oct. []en_US
dc.identifier.isbn978-1-5090-2580-0 (Online)
dc.description.abstractThe hypothesis that bent radio sources are supposed to be found in rich, massive galaxy clusters and the avalibility of huge amount of data from radio surveys have fueled our motivation to use Machine Learning (ML) to identify bent radio sources and as such use them as tracers for galaxy clusters. The shapelet analysis allowed us to decompose radio images into 256 features that could be fed into the ML algorithm. Additionally, ideas from the field of neuro-psychology helped us to consider training the machine to identify bent galaxies at different orientations. From our analysis, we found that the Random Forest algorithm was the most effective with an accuracy rate of 92% for a classification of point and extended sources as well as an accuracy of 80% for bent and unbent classificationen_US
dc.subjectAlgorithm design and analysisen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectFeature extractionen_US
dc.subjectRadio frequencyen_US
dc.titleClassifying bent radio galaxies from a mixture of point-like/extended images with machine learningen_US
dc.contributor.researchID24287717 - Oozeer, Nadeem

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