Point Process Modelling of Objects in the Star Formation Complexes of the M33 Galaxy
Abstract
In this thesis, Gibbs point process (GPP) models are constructed to study the spatial distribution of objects in the star formation complexes of the M33 galaxy. The GPP models circumvent the limitations of the two-point correlation function employed in the current astronomy literature by naturally accounting for the inhomogeneous distribution of these objects. The spatial distribution of these objects serves as a sensitive probe in understanding the star formation process, which is crucial in understanding the formation of galaxies and the Universe. The objects under study include the CO filament structure, giant molecular clouds (GMCs) and young stellar cluster candidates (YSCCs). A hierarchical model is adopted to account for the natural formation hierarchy among these objects. The effect of the properties of GMCs on their spatial correlation with YSCCs is also investigated. A Bayesian paradigm is employed for model inference. Potential physical implications are obtained and addressed through model criticism.