Abstract
The build up of stars over the history of the universe is important for understanding why our universe looks the way it does today: it is the stars that create the energy that lights up the night sky. This thesis looks into some of the drivers of this star formation over the history of the universe.
The connection between star formation rate and the mass of stars in galaxies is studied along with the connection between star formation and galaxy mergers. To do this, new techniques and tools are required for generating accurate star formation rate estimates and detecting galaxy mergers. A key component of star formation rate estimates, emission in the far-infrared, suffers from relatively low resolution which causes galaxies to blend with one another.
In this thesis, existing de-blending tools have been improved, allowing better extraction of far-infrared luminosities and hence better estimates of star formation rates. This thesis also employs the latest deep learning techniques to identify merging galaxies in both simulations and observations of our universe.
Studying the relation between the star formation rate and the existing mass of stars in galaxies found that in the early universe, high mass and low mass galaxies formed stars at similar rates. As the universe aged, high mass galaxies become less able to form new stars. For the influence of galaxy mergers on star formation rates, this thesis found that on average, galaxy mergers do not notably influence star formation rates but can trigger starbursts.
The connection between star formation rate and the mass of stars in galaxies is studied along with the connection between star formation and galaxy mergers. To do this, new techniques and tools are required for generating accurate star formation rate estimates and detecting galaxy mergers. A key component of star formation rate estimates, emission in the far-infrared, suffers from relatively low resolution which causes galaxies to blend with one another.
In this thesis, existing de-blending tools have been improved, allowing better extraction of far-infrared luminosities and hence better estimates of star formation rates. This thesis also employs the latest deep learning techniques to identify merging galaxies in both simulations and observations of our universe.
Studying the relation between the star formation rate and the existing mass of stars in galaxies found that in the early universe, high mass and low mass galaxies formed stars at similar rates. As the universe aged, high mass galaxies become less able to form new stars. For the influence of galaxy mergers on star formation rates, this thesis found that on average, galaxy mergers do not notably influence star formation rates but can trigger starbursts.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Supervisors/Advisors |
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| Award date | 22-Nov-2019 |
| Place of Publication | Groningen |
| Publisher | |
| Print ISBNs | 978-94-034-2128-5 |
| Electronic ISBNs | 978-94-034-2127-8 |
| DOIs | |
| Publication status | Published - 2019 |