The thesis is titled “Statistical Signal Processing Approach to Investigate Solar Internal Dynamics”. The research primarily investigates the internal dynamics of the Sun using statistical signal processing. The Solar Neutrino Flux Density time series obtained from the Sudbury Neutrino Observatory (SNO) and the Total Solar Irradiance (TSI) time series taken from the Earth Radiation Budget Satellite (ERBS) are used as the signals under investigation in this thesis. Solar Neutrino Flux Density, which originates from the Sun’s core, contains information about the Sun’s internal dynamics, whereas Total Solar Irradiance (TSI), which provides the energy that determines the Earth's climate, represents the dynamics of the Earth’s climate. The signals are pre-processed by smoothing and filtering before the nature of their persistence is determined. This major finding may help climatologists distinguish between solar and man-made influences on climate. The form of time dependence of the frequency content of the signals is found to determine the stationarity/non-stationarity behaviour of the signals. The underlying periodicities have also been investigated and compared with the periodicities for other solar activities reported by other scientists. The multifractal features of the signals are examined to capture the structural patterns of the signals. Efforts have been made to bring to light the complexity of the signals, which principally include understanding the nonlinear dynamics and chaos of the signals. A statistical link between the observed neutrino flux and the solar irradiance data, along with their mutual supportiveness, has been discovered in this research thesis.
- Quote paper
- Mofazzal Hossain Khondekar (Author), 2013, Statistical Signal Processing Approach to Investigate Solar Internal Dynamics, Munich, GRIN Verlag, https://www.grin.com/document/1692134
