Title:
Bayesave Analysis Study on Recovering Waveform Complexity through Reconstructions
Bayesave Analysis Study on Recovering Waveform Complexity through Reconstructions
| dc.contributor.advisor | Shoemaker, Deirdre | |
| dc.contributor.author | Day, Brian M. | |
| dc.contributor.committeeMember | Laguna, Pablo | |
| dc.contributor.department | Physics | |
| dc.date.accessioned | 2017-07-28T18:33:41Z | |
| dc.date.available | 2017-07-28T18:33:41Z | |
| dc.date.created | 2017-05 | |
| dc.date.issued | 2017-05 | |
| dc.date.submitted | May 2017 | |
| dc.date.updated | 2017-07-28T18:33:41Z | |
| dc.description.abstract | The field of gravitational wave astronomy is a means of observing the universe in a new way. Crucial to the success of this new astronomy is analyzing the data obtained from the Laser Interferometer Gravitational-Wave Observatory (LIGO). Gravitational waves are oscillations of spacetime that propagate to Earth. We can predict these waveforms using solutions of Einstein’s Equations from general relativity. There are several ways the LIGO scientific collaboration uses to detect signals. This thesis presents my work on one such method, Bayeswave analysis. Bayeswave analysis is one tool to process the data collected from the detectors. Bayeswave offers analysis on a potential event that is agnostic, in other words it is independent of theoretical predictions of the signal, due to it being a minimal assumption analysis and can be used to determine if the event is a signal, a glitch, or noise. Through the analysis, Bayeswave uses evidence values obtained from comparing signal, glitch, and noise models to determine what the event most likely is and produces reconstructions of both the signal and glitch models. This information can be used to further understand the event in the data. Although Bayeswave has shown to be able to accurately reconstruct simple waveforms, its ability to accurately reconstruct waveforms from systems with more complex initial parameters is not known. Therefore, this study is to determine if Bayeswave can accurately reconstruct known injected signals with varied initial parameter complexity. The ability for Bayeswave to reconstruct the more complex injected waveforms is gauged by analyzing the median overlap between the reconstruction and the injection as a function of signal-to-noise ratio (SNR), which is a gauge of how strong the signal is compared to background noise, for various inclination angles, the strain and frequency data as functions of time, and the residual strain of the reconstruction waveform when it is subtracted from the injected waveform as a function of time. | |
| dc.description.degree | Undergraduate | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/58497 | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Gravitational waves | |
| dc.subject | Model analysis | |
| dc.subject | Bayeswave analysis | |
| dc.subject | Binary black holes | |
| dc.title | Bayesave Analysis Study on Recovering Waveform Complexity through Reconstructions | |
| dc.type | Text | |
| dc.type.genre | Undergraduate Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Physics | |
| local.contributor.corporatename | Undergraduate Research Opportunities Program | |
| local.relation.ispartofseries | Undergraduate Research Option Theses | |
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| thesis.degree.level | Undergraduate |