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    An algorithmic interpretation of historic cosmic ray recordings

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    Date
    2018
    Author
    Mattana, V.W.F.
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    Abstract
    The aim of this study is to make additional GLE (ground level enhancement) data recordings from the 1940s and 1950s available, as well as generating of a veri ed, accurate synthetic ground truth image of historic cosmic ray ionization data recordings. These images can subsequently be used to test data extraction algorithms. The ground truth image is a reference point from which measurements can be made, concerning the tness of an algorithm. In this study, the ground truth image will be constructed from properties of the original image. This ground truth image will represent the data captured in the original image, and as such can be used to test data extraction algorithms on an image representative of the original data set, but with one major di erence; the ground truth image is known, and reproducible. It is not a perfect replica of the original but rather a similar image where only the properties of interest are accurately described. A synthetic version of the ground truth image must to some degree, reproduce the distortions and optical artefacts present in the original image. The creation of such an image is possible when using image processing techniques, such as binarization, correlation, morphology, segmentation, amongst other techniques. The detection algorithm is tested on the synthetic ground truth image to evaluate the detection capabilities of the algorithm, using measures such as MSE, precision, accuracy and recall. The ionization data of GLE #5 is extracted from the recordings of three stations, viz; Godhavn, Cheltenham, and Christchurch. The ionization data is then converted to percentage increase above background cosmic ray levels, for comparison to existing neutron monitor data. The neutron monitor data is sourced from a GLE database, as well as using Forbush's interpretation of the event.
    URI
    https://orcid.org/0000-0001-8551-1157
    http://hdl.handle.net/10394/31272
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    • Natural and Agricultural Sciences [2777]

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