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.
Contents
CHAPTER 1 INTRODUCTION
1.1 Introduction
1.2 Statistical Signal Processing
1.2.1 History of Signal Processing
1.2.2 Statistical Signal processing in Astrophysics and astronomy
1.3 The Sun
1.3.1 Needs to study the Sun
1.3.2 Solar Neutrino Flux
1.3.2.1 Solar Neutrino Detectors
1.3.3.1 Sudbury Neutrino Observatory
1.3.3 Total Solar Irradiance
1.3.3.1 Earth Radiation Budget Satellite
1.4 Brief preview of Earlier Works
1.5 Scope of the work
CHAPTER 2 DENOISING OF THE SIGNAL AND SCALING ANALYSIS
2.1 Introduction
2.2 Theory
2.2.1 Smoothing
2.2.2 Denoising using Discrete Wavelet Transform
2.2.3 Scaling Analysis
2.3 Result
2.4 Conclusion
CHAPTER 3 STATIONARY/NONSTATIONARY IDENTIFICATION AND SEARCH FOR PERIODITIES
3.1 Introduction
3.2 Theory
3.2.1 Smoothed Pseudo Wigner Ville Distribution
3.2.2 Search for periodicities
3.2.2.1 Discrete Fourier Transform
3.2.2.2 Hilbert Huang Transform
3.2.2.3 Peak Detection Algorithm in search of Prominent Peaks
3.2.2.3.1 Derivative Method
3.2.2.3.1.1 Method 1
3.2.2.3.1.2 Method 2
3.2.2.3.1.3 Method 3
3.2.2.3.2 Integral Method
3.2.2.3.2.1 Method 4
3.3 Results
3.4 Conclusion
CHAPTER 4 MULTIFRACTALITY AND SINGULARITY
4.1 Introduction
4.2 Theory
4.2.1 Multi-fractal Detrended Fluctuation Analysis (MFDFA)
4.2.2 Continuous Wavelet Transform
4.2.3 Wavelet Transform Modulus Maxima (WTMM)
4.2.4 Multifractal Analysis
4.2.4.1 For MFDFA
4.2.4.2 For WTMM
4.3 Results
4.4 Conclusion
CHAPTER 5 NONLINEARITY, CHAOSITY AND COMPLEXITY
5.1 Introduction
5.2 Theory
5.2.1 Delay Vector Variance (DVV) Analysis
5.2.2 “0-1” Test for Chaos
5.2.3 Correlation Dimension Analysis
5.2.4 Largest Lyapunov Exponent Method
5.2.5 Information Entropy
5.2.6 Recurrence Plot and Recurrence Quantification Analysis
5.2.6.1 Recurrence Plot
5.2.6.2 Recurrence Quantification Analysis
5.3 Results
5.4 Conclusion
CHAPTER 6 STATISTICAL ASSOCIATION BETWEEN TSI AND NEUTRINO FLUX
6.1 Introduction
6.2 Theory
6.2.1 Multifractal Detrended Cross Correlation Analysis
6.2.1.1 Multifractal Cross Correlation Detrended Fluctuation Analysis
6.2.1.2 Multifractal Cross Correlation Detrending Moving Average Analysis
6.2.1.3 Multifractal Parameters
6.2.2 Continuous Wavelet Transform based Semblance Analysis
6.2.3 Singularity Spectral Analysis (SSA) based Causality Test
6.2.3.1 SSA Algorithm
6.2.3.2 Causality Test
6.3 Results
6.4 Conclusions
CHAPTER 7 FINAL CONCLUSION
7.1 Conclusive Discussion
7.2 Future Scope
Research Objective and Focus Areas
The research focuses on the investigation of solar internal dynamics using statistical signal processing methods on Solar Neutrino Flux Density and Total Solar Irradiance (TSI) time series data, aiming to understand the underlying complex, non-linear, and non-stationary dynamics of the Sun.
- Application of statistical signal processing techniques to solar data
- Denoising and scaling analysis of solar irradiance and neutrino flux signals
- Identification of stationarity vs. non-stationarity and search for periodicities
- Multifractal and singularity analysis to assess signal complexity
- Investigation of nonlinearity, chaos, and statistical associations between solar parameters
Excerpt from the Book
1.1 Introduction
A stochastic process is a mathematical model for a phenomenon that evolves in time in an unpredictable manner from the viewpoint of the observer. The phenomenon may be a sequence of real-valued measurements of voltage or temperature, a binary data stream from a computer, a modulated binary data stream from a modem, a sequence of coin tosses, the daily Dow–Jones average, radiometer data or photographs from deep space probes, a sequence of images from a cable television, or any one of possible sequences, waveforms, or signals of any imaginable type. A discrete-time signal or time series is a set of observations taken sequentially in time, space, or in some other independent variable. We mainly concern discrete-time signal and the terms signal, time series, or sequences will be used to refer to a discrete-time signal. It may be unpredictable because of such effects as interference or noise in a communication link or storage medium, or it may be an information-bearing signal, deterministic from the viewpoint of an observer at the transmitter but non-deterministic to an observer at the receiver.
Natural randomness inherent to a non-deterministic system generates aleatory uncertainty in it. Stochastic processes usually occur in applications for the context of systems which change the processes to produce other processes. The intentional operation on a signal, produced by one process (an “input signal”), to produce a new signal (an “output signal”) is generally referred to as signal processing. Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the non-deterministic nature of the signal, statistical techniques play an important role in signal processing.
Summary of Chapters
CHAPTER 1 INTRODUCTION: This chapter provides an introductory overview of the research, including the history of signal processing in astrophysics and a detailed description of the Sun's structure and solar activity indices.
CHAPTER 2 DENOISING OF THE SIGNAL AND SCALING ANALYSIS: This chapter focuses on the preprocessing of solar data through smoothing and denoising, followed by an estimation of Hurst Exponents to determine signal persistency.
CHAPTER 3 STATIONARY/NONSTATIONARY IDENTIFICATION AND SEARCH FOR PERIODITIES: This chapter analyzes the signals for stationarity and uses transforms like DFT and HHT to detect underlying periodicities in the solar data.
CHAPTER 4 MULTIFRACTALITY AND SINGULARITY: This chapter characterizes the multifractal nature and self-similarity of the signals using MFDFA and WTMM methods to quantify non-linear dynamics.
CHAPTER 5 NONLINEARITY, CHAOSITY AND COMPLEXITY: This chapter investigates the existence of non-linearity and chaos in the signals using tests such as the “0-1” test, Lyapunov exponents, and recurrence quantification analysis.
CHAPTER 6 STATISTICAL ASSOCIATION BETWEEN TSI AND NEUTRINO FLUX: This chapter explores the statistical cross-correlation and causal relationships between solar irradiance and neutrino flux data using advanced methods like MF-DCCA and SSA.
CHAPTER 7 FINAL CONCLUSION: This chapter summarizes the main research findings and provides a discussion on future research directions in solar neutrino and irradiance analysis.
Key Terms
Statistical signal processing, Solar Neutrino Flux, Total Solar Irradiance, Time series analysis, Denoising, Discrete Wavelet Transform, Hurst Exponent, Multifractality, Singularity spectrum, Non-linearity, Chaos detection, Recurrence Quantification Analysis, Cross-correlation, Singular Spectral Analysis, Solar internal dynamics.
Frequently Asked Questions
What is the primary goal of this thesis?
The primary goal is to investigate the internal dynamics of the Sun by applying advanced statistical signal processing methods to neutrino flux and solar irradiance data.
What are the central themes of this research?
The central themes include identifying non-stationarity, quantifying complexity and non-linearity in solar data, and finding statistical evidence for associations between neutrino flux and solar irradiance.
What is the main research question?
The study aims to determine the underlying dynamics of the Sun, whether the solar neutrino flux and irradiance exhibit chaotic or regular behavior, and if there is a causal association between these two solar indicators.
Which scientific methods are utilized?
The thesis employs a variety of methods including Discrete Wavelet Transform (DWT), Finite Variance Scaling Method (FVSM), Hilbert Huang Transform (HHT), Multifractal Detrended Fluctuation Analysis (MFDFA), and Singular Spectral Analysis (SSA).
What topics are discussed in the main body?
The body covers data preprocessing, periodic detection, characterization of multifractal features, evaluation of non-linearity and chaos, and a formal causal relationship analysis between neutrino and irradiance signals.
How is the research characterized by keywords?
The research is characterized by terms such as statistical signal processing, solar neutrino flux, multifractality, and chaos, indicating a rigorous mathematical approach to solar physics.
Why are neutrino flux signals used in this research?
Neutrino flux signals originate from the Sun's core and provide direct, unscattered information about the solar interior, making them ideal for probing the solar engine.
What is the significance of the "0-1" test in this context?
The "0-1" test is a binary method used to robustly determine whether the solar data behaves as a deterministic chaotic system or a regular, non-chaotic system.
What findings relate to the relationship between TSI and Neutrino flux?
The study suggests a possible one-way statistical association where solar neutrino flux patterns may influence or support predictions for solar irradiance variations.
- Citar trabajo
- Mofazzal Hossain Khondekar (Autor), 2013, Statistical Signal Processing Approach to Investigate Solar Internal Dynamics, Múnich, GRIN Verlag, https://www.grin.com/document/1692134
