JavaScript is disabled for your browser. Some features of this site may not work without it.
  • About K
  • Academics
  • Admission
  • Alumni Relations
  • Giving to K
  • News & Events
  • Student Life
  • HORNET HIVE
  • ATHLETICS
  • SITEMAP
  • WEBMAIL
    • Login
    View Item 
    •   CACHE Homepage
    • Academic Departments, Programs, and SIPs
    • Physics
    • Physics Senior Integrated Projects
    • View Item
    •   CACHE Homepage
    • Academic Departments, Programs, and SIPs
    • Physics
    • Physics Senior Integrated Projects
    • View Item

    Automating the Detection of Magnetic Flux Ropes Using Data Mining Techniques

    Thumbnail
    View/Open
    Searchable PDF / Kalamazoo College Only (27.71Mb)
    Date
    2014
    Author
    Akhavantafti, Mojtaba
    Metadata
    Show full item record
    Abstract
    Our reliance on space-borne and ground-based technological systems has made us susceptible to the adverse conditions in the space environment. These conditions can cause disruption of satellite operations, electric power distribution grids, and oil pipelines, leading to a variety of socioeconomic losses. In order to mitigate the impact, we need to better understand the planetary magnetic field and their responses to the solar wind. In particular, magnetic reconnection is a widely-studied product of the Earth-Sun interaction and it is believed to be the ultimate driver of extreme phenomena such as eruptive solar flares, coronal mass ejection, geomagnetic storms, and magnetospheric substorms. To better understand the birth and the evolution of magnetic reconnection, one can study the dynamics of the magnetic field in the magnetotail and look for events such as magnetic flux ropes. This paper aims to investigate the GEOTAIL mission magnetic data as a function of time and location and to search for magnetic flux ropes. It further explores the sensitivity and the specificity of automating the process of magnetic flux rope detection. In conclusion, our approach indicated satisfying degrees of accuracy (~ 80%), yet work needs to be done to better train the algorithms to increase the accuracy (> 95%) of the detection process.
    URI
    http://hdl.handle.net/10920/30050
    Collections
    • Physics Senior Integrated Projects [335]

    Browse

    All of CACHECommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV
    Logo

    Kalamazoo College
    1200 Academy Street
    Kalamazoo Michigan 49006-3295
    USA
    Info 269-337-7000
    Admission 1-800-253-3602

    About K
    Academics
    Admission
    Alumni Relations
    Giving to K
    News & Events
    Student Life
    Sitemap
    Map & Directions
    Contacts
    Directories
    Nondiscrimination Policy
    Consumer Information
    Official disclaimer
    Search this site


    Academic Calendars
    Apply
    Bookstore
    Crisis Response
    Employment
    Library
    Registrar
    DSpace Express is a service operated by 
    Atmire NV