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Title: Towards precision measurements of large-scale structure with next-generation spectroscopic surveys
Author: Farr, James Alexander
ISNI:       0000 0005 0289 3102
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2021
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Over recent decades, spectroscopic surveys have yielded exceptional measurements of the large-scale structure of the Universe. Notably, they have measured the baryon acoustic oscillation (BAO) scale at late times, helping to develop a tightly-constrained cosmological model by complementing measurements from the cosmic microwave background. In this thesis, we focus on late-time BAO measurements from quasar (QSO) spectra. Such measurements can be made at 1 < z < 2 via direct QSO clustering, and at 2 < z < 4 via the Lyman-alpha (Lya) forest extracted from high-z QSO spectra. In the near future, the Dark Energy Spectroscopic Instrument (DESI) will continue to advance this field, increasing the quantity and quality of QSO spectra available via a host of technological improvements. In order to maximise the impact of its data, however, DESI will require major advances in analysis methods to be made. This thesis describes work to develop such methods for use in two areas of DESI's QSO survey. First, we address the construction of optimal strategies for classifying QSO target spectra. We use data from existing surveys to demonstrate the performance of potential strategies, finding that high performance levels can be achieved using existing classification tools. Next, we present LyaCoLoRe, a package developed to produce mock Lya forest datasets from simple simulations, to be used in Lya BAO analyses. We describe the methods employed by LyaCoLoRe, and demonstrate that our mocks are suitable to be used in Lya BAO studies present and future. We then discuss applications of the classification strategies and mock datasets presented previously, as well as a method of using BAO measurements to constrain the local cosmic expansion rate, showing results from current datasets and providing forecasts for DESI. We conclude by highlighting a number of future paths which our work could follow, with particular focus on the opportunities that will emerge from DESI.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available