In this thesis, the Initial Mass Function (IMF) is studied using the Stochastically Lighting Up Galaxies software suite (SLUG), a package of tools including a stochastic Stellar Population Synthesis (SPS) code and associated analysis packages, including a novel Bayesian inference framework. Following an introduction to some core concepts, new developments of the SLUG code are described. These include a variable IMF capability which is then applied to broad-band photometry taken from the Legacy ExtraGalactic Ultraviolet Survey (LEGUS), a Hubble Space Telescope treasury programme. The physical parameters of star clusters in galaxy NGC 628 are inferred using SLUG's Bayesian inference tools. We find that the posterior probability distributions of the high-mass slope of the IMF are very broad, and we quantify a degeneracy between the IMF and the cluster mass. The inclusion of additional photometric data (Ha) is found to provide some improvement. However, using mock cluster models we found that only through constraining the mass of the cluster through photometrically-independent means is it possible to accurately recover the IMF slope. An additional source of information is the UV spectrum, which is dominated by the massive stars whose populations are affected by the high-mass region of the IMF. To be able to exploit this region of the spectrum effectively using equivalent width measurements, the resolution of SLUG's UV spectral synthesiser requires improvement. To this end, the implementation of a high-resolution UV synthesiser is described, and then put to use in a theoretical study of the IMF using mock observations generated with SLUG. The constraining power of UV spectral features when combined with broad-band photometry is quantified, resulting in significant improvement in IMF slope recovery. Finally the results and limitations of the studies are discussed, and recommendations are made for future studies and improvements.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:753777 |
| Date | January 2018 |
| Creators | Ashworth, Greg |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/12739/ |
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