Dorrington, Iain
2019.
Improved methods for the detection of gravitational waves associated with gamma-ray bursts.
PhD Thesis,
Cardiff University.
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Abstract
In this thesis we will see two targeted searches for gravitational waves (GWs) as-sociated with gamma-ray bursts (GRBs) and how to make them faster and more sensitive. The first of these is PyGRB, a matched filter search that follows up short GRB detections. The second is X-pipeline, a burst search for both long and short GRBs. We will begin with a chapter on GW background, where we will show that GWs are a consequence of general relativity, discuss sources of GWs, and look into the basics of GW detectors. In chpater 3, we summarise the current state of GRB science before looking at multi-messenger astronomy and what it can teach us about the universe. Chapter 4 is on PyGRB. This chapter begins by looking at the theory behind how the pipeline works, and then looks at how PyGRB works in practice. This chapter ends with a summary of the results from the PyGRB search of the latest observing run O2. We then look at PyGRB development work. The code used in O2 is now dated and needs to be rewritten to use modern software tools. We will look at the work that has already been done to update PyGRB and the speed improvements it brings, learn why it is scientifically important that the code runs quickly, and see some of the new tools that we have made available. We end by looking at X-pipeline. We will start with the theory behind how X-pipeline works and looking at the results from the X-pipeline analysis of GRBs in O2. Then we will look at how machine learning can be used to improve the sensitivity and speed of X-pipeline. We end this chapter with a discussion of how to improve the search and the issues that arise from using machine learning.
Item Type: | Thesis (PhD) |
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Date Type: | Submission |
Status: | Unpublished |
Schools: | Physics and Astronomy |
Subjects: | Q Science > QB Astronomy Q Science > QC Physics |
Uncontrolled Keywords: | Gravitational waves, Gamma-ray Bursts, multi-variate analysis, machine learning, multi-messenger astronomy. |
Funders: | STFC |
Date of First Compliant Deposit: | 2 January 2020 |
Last Modified: | 20 Oct 2020 01:34 |
URI: | https://orca.cardiff.ac.uk/id/eprint/128124 |
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