Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
G., Dal Canton T., 2020, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2020arXiv200300357M p
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
method 2
citation-polarity summary
representative citing papers
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.
Simulations indicate joint Taiji+LISA analysis of five SLGW events yields H0 95% credible interval uncertainties of 0.11 (source redshift unknown) or 0.042 (source redshift known).
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
citing papers explorer
-
Neural posterior estimation of Galactic Binary signals for the LISA mission
Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
-
Inpainting over the cracks: challenges of applying pre-merger searches for massive black hole binaries to realistic LISA datasets
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
-
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
-
Pre-localization of Massive Black Hole Binaries in the Millihertz Band
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
-
Probing Kerr Symmetry Breaking with LISA Extreme-Mass-Ratio Inspirals
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.
-
Measuring the Hubble constant with strongly lensed gravitational waves from space-based detector networks
Simulations indicate joint Taiji+LISA analysis of five SLGW events yields H0 95% credible interval uncertainties of 0.11 (source redshift unknown) or 0.042 (source redshift known).
-
Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.