Abstract
AbstractEvolutionary population synthesis models have been instrumental to our understanding of galaxy formation and evolution. For the successful ap- plication of these models, they must be underpinned by a comprehensive catalogue of theoretical or empirical stellar spectra. In the latter case, spec- tra must be described by certain atmospheric parameters: effective temper- ature (T eff), surface gravity (log g) and chemical abundance ([Fe/H] and [α/Fe]). Thus, to accurately model the spectrophotometric properties of distant galaxies, we must first study the stars around us. In this thesis we utilise the MaNGA Stellar Library (MaStar), an SDSS-IV survey of 80,592 spectra representing 27,945 unique stars, to create the basis for the next- generation of stellar population models for extragalactic astronomy.
We have developed a stellar parameter pipeline that fits theoretical spec- tra from a combination of model atmosphere libraries to each MaStar ob- servation using the penalised pixel-fitting method (pPXF). We match the MaStar observations to Gaia photometry and use this to set reliable, flat priors for Teff and log g. The stellar atmospheric parameters and their uncer- tainties are then estimated with a Bayesian approach in combination with the Markov Chain Monte Carlo (MCMC) algorithm. Initially, we calcu- lated stellar parameters using solar-scaled [α/Fe] models, which was then expanded to include [α/Fe] as a fourth parameter. Our method is corrobo- rated by its application to well known stars, such as the Sun and Vega, by comparing our results to several other literature sources of stellar parameters and by the parallel development and testing of stellar population models on star clusters. By applying quality flags based on MCMC convergence and the χ2 of the model fit, we recover the following parameter distributions for
52,276 spectra: 2913 ≤ Teff ≤ 26096 K, −0.5 ≤ log g ≤ 5.4, −2.5 ≤ [Fe/H]
≤ 0.7. For a sample of 17,214 spectra, we estimate [α/Fe] in the range of
−0.25 < [α/Fe] < 0.48.
We further leverage the vastness of MaStar by identifying carbon- and oxygen-rich, thermally-pulsing asymptotic giant branch (TP-AGB) stars for their inclusion in stellar population models. This research is done using the SDSS photometric bands (u, g, r, i, z ) due to time efficiency and as a proof of application to future large photometric surveys, such as the Legacy Survey of Space and Time (LSST). We apply an unsupervised machine learning technique called t-distributed stochastic neighbour embedding to test its ability in TP-AGB identification. This method is able to isolate such stars and correlates well with the atmospheric parameters. We then define a novel colour selection of (g − r) > 2 and (g − i) < 1.55(g − r) −0.07, based solely on SDSS photometry. This is compared to literature colour cuts and a supervised learning, support vector machine (SVM) which we have trained. We find the SVM method most efficient, recovering an average F1-score, the chosen performance metric, of 0.82 over 200 test cases. Through such methods we are able to identify 69 carbon-type and 118 oxygen-type spectra in MaStar. Lastly, we determine the spectral types of MaStar TP-AGB stars using empirical templates and a discrete version of our stellar parameter fitting routine.
The application of MaStar spectra has marked a new era in galaxy mod-
elling, allowing for the study of complex stellar populations over a wide wavelength domain. The latest MaStar-based stellar population models, us- ing the atmospheric parameters presented here, have already been applied to the SDSS MaNGA data analysis pipeline and can be used for future surveys by probing the optical and near-infrared window.
| Date of Award | 13 Feb 2023 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Daniel Thomas (Supervisor), Claudia Maraston (Supervisor) & Nicholas John Savage (Supervisor) |