Reverberation Mapping in Bulk with OzDES RM: A Statistical Approach to Measuring the Geometry of Active Galactic Nuclei with Light Echoes




Sommer, Natalia

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Active galactic nuclei (AGN) are among the most luminous objects known to humans, and are ubiquitous in the local and distant Universe. It is widely accepted they host a supermassive black hole in their centre, however, the surroundings of the supermassive black holes are unknown due to a combination of the relatively small scales of these regions, and the spatial resolution limits of current instruments. The method of reverberation mapping (RM) aims to bypass these limitations and infer central AGN geometry through the analysis of light-echoes within these regions. By studying the light-signals emerging at different locations within the nuclei at different times, RM can provide insight to the structure of AGN. In this thesis we investigate RM in bulk, or industrial scale RM, a relatively unexplored area becoming more relevant with the use of multi-object spectrographs for RM programs, which allows monitoring of a significantly higher number of AGN than previously. RM in bulk applies statistical methods to combine data from multiple sources to achieve high-quality results when the outcomes of individual sources are sub-par. We investigate this methodology using simulated mock data, and apply it to observations made by the Australian Dark Energy Survey Reverberation Mapping (OzDES RM) program. We find what appears to be a discrepancy between the results of simulated and observed data, which could suggest that current models used for mock data generation need improvement. Additionally, we examine the survey strategy of OzDES RM and its impact on achieving reliable RM results, and demonstrate that previous simulations may have underestimated the problems of the adopted strategies. Based on our results, we argue that a deeper analysis of the models used for our simulations is needed, to ensure more robust predictions about RM results. Furthermore, we recommend future RM programs reassess their survey strategies to secure high-quality results for the advancement of the field, and the applications thereof.






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