Large spectroscopic surveys are trailblazing endeavours in the study of stellar archaeology
and near eld cosmology. Access to homogeneous databases of thousands
of stellar spectra allow for a detailed and statistically satisfying look into the chemical
abundance distribution of our Galaxy and its surrounding satellites, ultimately
working towards a better understanding of galactic chemical evolution. This thesis
presents the work of three new studies at the current frontier of stellar archaeology.
Through the rst look at carbon-enhanced metal-poor (CEMP) stars using H-band
spectra, six new CEMP stars and another seven likely candidates were found within
the APOGEE database following Data Release 12. These stars have chemical compositions
typical of metal-poor halo stars, however the alpha-abundances of two stars
indicate possible origins in an accreted dwarf galaxy. A lack of heavy element spectral
lines impedes further sub-classi cation of these CEMP stars, however, based
on radial velocity scatter, we predict most are not CEMP-s stars which are typically
found in binary systems. This preliminary investigation warrants optical observations
to con rm the stellar parameters and low metallicities of these stars, to determine the
heavy-element abundance ratios and improve the precision in the derived abundances,
and to examine their CEMP sub-classi cations. Additionally, the rst results for the
spectroscopic follow up to the Pristine survey are presented. Using a sample of 149
stars, a success rate of 70% for finding stars with [Fe/H]<-2.5 and 22% for finding
stars with [Fe/H]<-3.0 is reported, significantly higher than other surveys that typically
report success rates of 3-4% for recovering stars with [Fe/H]<-3.0. Finally, the new spectral analysis tool StarNet is introduced. A deep neural network architecture
is used to examine both synthetic stellar spectra and SDSS-III APOGEE spectral
data and can produce the stellar parameters of temperature, gravity, and metallicity
with similar or better precision as the APOGEE pipeline values when trained directly
with the APOGEE spectra. StarNet is capable of being trained on synthetic data as
well, and is able to reproduce the stellar parameters for both synthetic and APOGEE
spectra, including low signal-to-noise spectra, with similar precision to training on the
APOGEE spectra itself. The residuals between StarNet predictions and APOGEE
DR13 parameters are similar to or better than the di erences between the APOGEE
DR13 results and optical high resolution spectral analyses for a subset of benchmark
stars. While developed using the APOGEE spectral database (real spectra and
corresponding ASSET synthetic data with similar normalization functions), StarNet
should be applicable to other large spectroscopic surveys like Pristine. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/8646 |
| Date | 04 October 2017 |
| Creators | Kielty, Collin Louis |
| Contributors | Venn, Kimberley Ann |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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