Asteroseismology: Data Analysis Methods and Interpretation for Space and Ground-based Facilities

TL;DR

This dissertation reviews asteroseismology, focusing on data analysis methods for solar-like stars, including statistical inference and pipeline implementation for Kepler data, advancing the field's observational and analytical techniques.

Contribution

It provides a comprehensive overview of data analysis methods in asteroseismology and introduces a pipeline for Kepler data mode analysis, enhancing observational data interpretation.

Findings

Compilation of current data analysis techniques

Implementation of a Kepler data analysis pipeline

Discussion of Bayesian and frequentist statistical methods

Abstract

This dissertation has been submitted to the Faculdade de Ci\^encias da Universidade do Porto in partial fulfillment of the requirements for the PhD degree in Astronomy. The scientific results presented herein follow from the research activity performed under the supervision of Dr. M\'ario Jo\~ao Monteiro at the Centro de Astrof\'isica da Universidade do Porto and Dr. Hans Kjeldsen at the Institut for Fysik og Astronomi, Aarhus Universitet. The dissertation is composed of three chapters and a list of appendices. Chapter 1 serves as an unpretentious and rather general introduction to the field of asteroseismology of solar-like stars. It starts with a historical account of the field of asteroseismology followed by a general review of the basic physics and properties of stellar pulsations. Emphasis is then naturally placed on the stochastic excitation of stellar oscillations and on the potential of asteroseismic inference. The chapter closes with a discussion about observational techniques and the observational status of the field. Chapter 2 is devoted to the subject of data analysis in asteroseismology. This is an extensive subject, therefore, a compilation is presented of the relevant data analysis methods and techniques employed contemporarily in asteroseismology of solar-like stars. Special attention has been drawn to the subject of statistical inference both from the competing Bayesian and frequentist perspectives. The chapter ends with a description of the implementation of a pipeline for mode parameter analysis of Kepler data. In the course of these two first chapters, reference is made to a series of articles led by the author (or otherwise having greatly benefited from his contribution) that can be found in Appendices A to E. Chapter 3 then goes on to present a series of additional published results.