Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Statistics and Actuarial Sciences

Supervisor

McLeod, Ian A.

2nd Supervisor

Barmby, Pauline

Joint Supervisor

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.

Summary for Lay Audience

The star formation process is crucial in understanding the formation of galaxies and the Universe. Stars are understood to form in an aggregated manner from their stellar nurseries --- giant molecular clouds, leading to the formation of compact groups of stars called star clusters. Since the time scale of star formation surpasses human lifetime by orders of magnitude, studying the spatial distribution of giant molecular clouds, stars, and star clusters then serves as an indirect but sensitive probe for understanding the formation of these objects. While the spatial distribution of stars is relatively well-understood, this is not the case for giant molecular clouds or star clusters. In the current astronomy literature, the two-point correlation function is used for studying the spatial distribution of star clusters. However, it poses severe limitations and drawbacks when applied to studying the highly complex distribution of giant molecular clouds and star clusters. To address this issue, I adopt the framework of Gibbs point process models from spatial statistics and study its performance when applied to the point patterns of giant molecular clouds and young star clusters in the nearby M33 galaxy. Potential physical implications for the star formation process obtained from the models are also addressed.

Available for download on Monday, August 31, 2020

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