• Login
    View Item 
    •   NWU-IR Home
    • Conference Papers
    • Conference Papers - Potchefstroom Campus
    • View Item
    •   NWU-IR Home
    • Conference Papers
    • Conference Papers - Potchefstroom Campus
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Classifying bent radio galaxies from a mixture of point-like/extended images with machine learning

    Thumbnail
    Date
    2016
    Author
    Bastien, David
    Oozeer, Nadeem
    Somanah, Radhakrishna
    Metadata
    Show full item record
    Abstract
    The 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 classification
    URI
    http://hdl.handle.net/10394/20784
    http://dx.doi.org/10.1109/RADIO.2016.7772037
    http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7772037
    Collections
    • Conference Papers - Potchefstroom Campus [534]
    • Faculty of Natural and Agricultural Sciences [4311]

    Copyright © North-West University
    Contact Us | Send Feedback
    Theme by 
    @mire NV
     

     

    Browse

    All of NWU-IR Communities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Copyright © North-West University
    Contact Us | Send Feedback
    Theme by 
    @mire NV