In this thesis I show how large galaxy surveys, in particular the study of the properties of galaxies, can shed light on gravitational wave sources and dark matter. This is achieved using the latest data from the Dark Energy Survey, an on-going 5000 deg2 optical survey. Galaxy properties such as photometric redshifts and stellar masses are derived through spectral energy distribution fitting methods. The results are used to study host galaxies of gravitational wave events and how light traces dark matter in galaxy clusters. Gravita- tional wave (GW) science, and particularly the electromagnetic follow up of these events, is transforming what had never been seen into a new astronomical field able to unveil the nature of cataclysmic events. Identifying the galaxies that host these events, and es- timating their redshift, stellar mass, and star–formation rate, is crucial for cosmological analysis with gravitational waves, for follow up studies and to understand the formation of the binary systems that are thought to produce observable gravitational wave signals. This thesis describes how the host matching is implemented within the DES–GW pipeline and how observations of NGC 4993, the galaxy host of the event GW170817, provide important information about possible formation scenarios for binary neutron stars. In particular, we find that NGC 4993 presents shell structures and we relate their formation to the binary formation. The same galaxy properties are used to derive an observable mass proxy for galaxy clusters. I show that this mass observable correlates well with the total mass of clusters, which is mainly composed of dark matter. It can therefore be used for cosmological studies with galaxy clusters. The measurement of stellar–to–halo mass relations in clusters provides insights on the connection between the star content and the total matter content in clusters, and how this evolves over cosmic time.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:756323 |
| Date | January 2018 |
| Creators | Palmese, Antonella |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/10055879/ |
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