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There is no abstract available for this thesis. / Department of Physics and Astronomy

Star and stellar cluster formation in spiral galaxies is one of the biggest questions of astrophysics. In this thesis, I study how star formation, and the formation of stellar clusters, proceeds using SPH simulations. These simulations model a region of 400 pc and 107 solar masses. Star formation is modelled through the use of sink particles which represent small groups of stars. Star formation occurs in high density regions, created by galactic spiral arm passage. The spiral shock compresses the gas and generates high density regions. Once these regions attain sufficiently high density, self-gravity becomes dominant and drives collapse and star formation. The regions fragment hierarchically, forming local small groups of stars. These fall together to form clusters, which grow through subsequent mergers and large scale gas infall. As the individual star formation occurs over large distances before forming a stellar cluster, this process can result in significant age spreads of 1-2 Myrs. One protocluster is found to fail to merge due to the large scale tidal forces from the nearby regions, and instead expands forming a dispersed population of young stars such as an OB association.

This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / School of Environmental Sciences / M. Sc. (Astronomy)

This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / Environmental Sciences / M. Sc. (Astronomy)