LIGO Document T2300223-v1

Testing Specific Theories of General Relativity with LIGO

Document #:
LIGO-T2300223-v1
Document type:
T - Technical notes
Other Versions:
Abstract:
Gravitational waves observed by LIGO allow us to test general relativity in the strong-field regime
with populations of binary merger events. Observations thus far are consistent with general relativity at
both the individual and population level. Current tests of general relativity utilize a single deviation parameter rather than generic deviation parameters, which makes it difficult to map this information to specific
theories and robustly test them over ensembles of events. We apply Bayesian inference to the inspiral phase
of gravitational-wave signals in binary black hole merger events, to obtain posterior distributions for the
15 source parameters and 10 post-Newtonian deviation parameters. This parameter estimation involves the
hybrid sampling method which uses nested sampling to seed parallel-tempered Markov Chain Monte Carlo
(MCMC) ensembles and allows us to explore degenerate parameter spaces. We apply a Principal Component Analysis (PCA) to reduce the dimensionality of the parameter space, to understand the underlying
correlations between the deviation parameters. Hierarchical inference could then be applied to ensembles
of events to test specific theories beyond general relativity, such as the dynamical Chern Simons (dCS)
and Einstein-dilaton Gauss-Bonnet (EdGB) theories. This would result in constraining the bounds on the
coupling coefficients that characterize these specific theories.
Files in Document:
  • Final Report (Testing_Specific_Theories_Beyond_GR_with_LIGO_Final_Re...pdf, 2.1 MB)
Other Files:
  • Final Presentation (Testing_Specific_Theories_Beyond_GR_with_LIGO_Final_P...pptx, 4.7 MB)
  • Interim Report 1 (Testing_Specific_Theories_Beyond_GR_with_LIGO_Interim_...pdf, 317.2 kB)
  • Interim Report 2 (Testing_Specific_Theories_Beyond_GR_with_LIGO_Interim_...pdf, 1.5 MB)
  • Proposal (Testing_Specific_Theories_Beyond_GR_with_LIGO_Final (1).pdf, 96.6 kB)
Keywords:
SURF23

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