Ciuca, Ioana;
(2020)
Inference in the Milky Way in the Gaia era.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
We employ state-of-the-art statistical inference and Machine Learning techniques to understand the formation and evolution history of our Galaxy, the Milky Way, using data from the astrometric Gaia mission and ground-based spectroscopic surveys. We first investigate the vertical metallicity gradients of five mono-age stellar populations for a sample of 18,435 dwarf stars selected from the cross-matched Tycho- Gaia Astrometric Solution (TGAS) and RAdial Velocity Experiment (RAVE) Data Release 5. We find an increasingly steeper negative vertical metallicity gradient for the older stellar populations and a steadily increasing intrinsic dispersion in metallicity with age. These results are consistent with a scenario that thin disc stars formed from a flaring thin star-forming disc. To further study the chrono-chemo- dynamical structure of the Galactic disc, we develop a Bayesian Machine Learning framework called BINGO (Bayesian INference for Galactic archaeOlogy), which is a Bayesian Neural Network trained on asteroseismic age data, to obtain accurate relative stellar age estimates with reliable uncertainties for the Apache Point Obser- vatory Galactic Evolution Experiment (APOGEE) stars. After carefully architecting a training set to minimise bias, we apply BINGO to a stellar sample consisting of 17,305 carefully selected evolved stars. We find that the outer disc follows a differ- ent chemical evolution pathway than the inner disc. The outer metal-poor stars only starting to form after the compact thick disc formation phase has completed in the inner region and the star-forming gas disc extended outwardly with a metal-poor gas accretion. Using the Gaia DR2 data, we also try to find dwarf galaxies in eight Fermi-LAT extended, unassociated, gamma-ray source fields, to test the hypothesis that they owe to dark matter annihilation. After probing previously unexplored heliocentric distances of less than 20 kpc with an extreme-deconvolution technique, we find no sign of a dwarf galaxy in any of these fields despite Gaia’s excellent astrometric accuracy.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Inference in the Milky Way in the Gaia era |
| Event: | UCL |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10116405 |
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