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Virginia Tech

Computation Methods for Parametric Analysis of Gravitational Wave Data

Abstract

dc:description.abstract

Gravitational waves are detected, analyzed and matched filtered based on an approximation of General Relativity called the Post Newtonian theory. This approximation method is based on the assumption that there is a weak gravity field both inside and around the body. However, scientists cannot justify why Post-Newtonian theory (meant for weak fields) works so well with strong fields of black hole mergers when it really should have failed [C. Will 2011]. Yunes and Pretorius gave another approach called parameterized post-Einsteinian (ppE) theory that uses negligible assumptions and promises to identify any deviation on the parameters through post-processing tests. This thesis project proposes to develop a method for the parametric detection and testing of gravitational waves by computation of ppE for the inspiral phase using ChirpLab. A set of templates will be generated with ppE parameters that can be used for the testing.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Electrical Engineering
Department dc:contributor.department
Electrical Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Patel, Heta Ajay
Chairs dc:contributor.committeechair
  • Ruohoniemi, J. Michael
  • Patton, Lydia K.
Committee member dc:contributor.committeemember
  • Beex, Aloysius A.

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:22237
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/93766

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Patel, Heta Ajay. Computation Methods for Parametric Analysis of Gravitational Wave Data. masters thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93766