MSc-whistler-waves-detector
Lightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earthβs surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [5]. The resulting high occurrence rate of whistler events makes it impossible to identify and characterise them in a reasonable time. Therefore the automatic detection and characterisation of whistlers are valuable to the study of energetic particle dynamics in the plasmasphere and to develop models for operational use. Lichtenberger [1] developed an automatic detector and analyser based on the Appleton-Hartree dispersion relation and experimental models of particle density distribution. Recent advances in artificial neural network-based image processing methods for example, convolutional networks [6] may be able to provide an alternative method for the automatic identification and characterisation of whistler events in broadband VLF spectra. Model development is based on training a neural-network-based model on a large set of spectrograms with whistler events identified by the nodes of the Automatic Whistler Detection and Analysis Network (AWDAnet [7]). Spectrograms will be presented in the form of images (Figure 1.1) to take advantage of the wide range of image-processing techniques available for this type of object identification.
