A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.
B´ ecsy, N
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Searching for gravitational waves in pulsar timing array data is computationally intensive. The data is unevenly sampled, and the noise is heteroscedastic, necessitating the use of a time-domain likelihood function with attendant expensive matrix operations. The computational cost is exacerbated when searching for individual supermassive black hole binaries, which have a large parameter space due to the additional pulsar distance, phase offset and noise model parameters needed for each pulsar. We introduce a new formulation of the likelihood function which can be used to make the Bayesian analysis significantly faster. We divide the parameters into projection and shape parameters. We then accelerate the exploration of the projection parameters by more than four orders of magnitude by precomputing the expensive inner products for each set of shape parameters. The projection parameters include nuisance parameters such as the gravitational wave phase offset at each pulsar. In the new scheme, these troublesome nuisance parameters are efficiently marginalized over using multiple-try Markov chain Monte Carlo sampling as part of a Metropolis-within-Gibbs scheme. The acceleration provided by our method will become increasingly important as pulsar timing datasets rapidly grow. Our method also makes sophisticated analyses more tractable, such as searches for multiple binaries, or binaries with non-negligible eccentricities.
representative citing papers
A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
Simulations of continuous-wave searches show that PTA data first constrain GW frequency and strain amplitude together, then sky location, with chirp mass and inclination following later for evolving sources, with precision depending on source frequency and sky position.
Simulations of PTA data show that a full gravitational-wave signal template achieves the highest Bayes factors and most robust parameter estimation for individual supermassive black hole binaries compared to an Earth-term template and a novel Spike Pixel cross-correlation model.
citing papers explorer
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Population statistics of nanohertz gravitational wave sources
A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.
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Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference
A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
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Expectations for the first supermassive black-hole binary resolved by PTAs II: Milestones for binary characterization
Simulations of continuous-wave searches show that PTA data first constrain GW frequency and strain amplitude together, then sky location, with chirp mass and inclination following later for evolving sources, with precision depending on source frequency and sky position.
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Expectations for the first supermassive black-hole binary resolved by PTAs I: Model efficacy
Simulations of PTA data show that a full gravitational-wave signal template achieves the highest Bayes factors and most robust parameter estimation for individual supermassive black hole binaries compared to an Earth-term template and a novel Spike Pixel cross-correlation model.