REVIEW 2 cited by
MCMC Exploration of Supermassive Black Hole Binary Inspirals
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The Laser Interferometer Space Antenna will be able to detect the inspiral and merger of Super Massive Black Hole Binaries (SMBHBs) anywhere in the Universe. Standard matched filtering techniques can be used to detect and characterize these systems. Markov Chain Monte Carlo (MCMC) methods are ideally suited to this and other LISA data analysis problems as they are able to efficiently handle models with large dimensions. Here we compare the posterior parameter distributions derived by an MCMC algorithm with the distributions predicted by the Fisher information matrix. We find excellent agreement for the extrinsic parameters, while the Fisher matrix slightly overestimates errors in the intrinsic parameters.
Forward citations
Cited by 2 Pith papers
-
Detectability and Parameter Estimation for Einstein Telescope Configurations with GWJulia
A new open-source Julia tool forecasts Einstein Telescope parameter-estimation accuracy, finding the 2L45 design marginally best for single parameters but comparable to other layouts when joint precision is required.
-
DeepHMC : a deep-neural-network acclerated Hamiltonian Monte Carlo algorithm for binary neutron star parameter estimation
A neural-network surrogate for the log-likelihood gradients makes Hamiltonian Monte Carlo trajectories 30 times faster than relative-binning gradients and recovers LVK-consistent posteriors for two binary neutron star...
Discussion (0). Continue with ORCID to comment.