All gravitational-wave binaries analyzed through O4a are consistent with zero line-of-sight acceleration, measured with a new time-domain Doppler method applicable to any waveform model.
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PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
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abstract
We introduce new modules in the open-source PyCBC gravitational- wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.
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representative citing papers
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
Presents a practical fully time-domain end-to-end likelihood for gravitational-wave inference with structured linear algebra and GPU acceleration.
For a loud stellar-mass black hole binary, adding realistic orbital eccentricity to multiband Bayesian analysis weakens dipole-radiation constraints to |b|≲10⁻⁷, with strong degeneracies among dipole strength, chirp mass, and eccentricity.
GreyRing model based on greybody factors reproduces numerical relativity ringdown signals with mismatches of order 10^{-6} and enables a new post-merger consistency test of general relativity applied to GW250114.
A hierarchical analysis of 69 GWTC-3 black-hole merger candidates yields an effective compactness consistent with C=0.5 and no evidence for a low-compactness exotic population.
F-statistic framework analytically maximizes over distance and polarization to enable faster Bayesian inference of compact binary coalescences with a new evidence formulation that matches full frequency-domain results at lower cost.
Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.
Unmodeled point-mass lensing produces a spurious nonzero graviton mass posterior in GW231123 that vanishes when lensing is included in the analysis.
Future CE+ET detectors may detect lensed BNS kilonovae at ~0.5/yr via pointed follow-up of known galaxy lenses, while lensed sGRBs and afterglows remain rare or undetectable with current-generation facilities.
Bilby-antiglitch jointly models astrophysical signals and quasi-physical glitches to recover true source properties from simulated gravitational wave data contaminated by loud non-Gaussian transients.
In a controlled binary-black-hole benchmark, soft learned artifact-aware interval rescaling (LAIR) reduces marginal calibration error for frequency masks from 0.1195 to 0.0672 but is not uniformly better than raw intervals and is positioned as a diagnostic rather than a replacement for full posterio
Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.
GWTC-2.1 adds eight new high-significance compact binary coalescence events to the prior catalog, extending the observed black hole mass range and including candidates inside the pair-instability mass gap.
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
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Constraints on Line-of-Sight Acceleration from O1-O4
All gravitational-wave binaries analyzed through O4a are consistent with zero line-of-sight acceleration, measured with a new time-domain Doppler method applicable to any waveform model.
-
Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
-
Accelerated Time-domain Analysis for Gravitational Wave Astronomy
Presents a practical fully time-domain end-to-end likelihood for gravitational-wave inference with structured linear algebra and GPU acceleration.
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Constraining Dipole Radiation with Multiband Gravitational Waves from Eccentric Binary Black Holes
For a loud stellar-mass black hole binary, adding realistic orbital eccentricity to multiband Bayesian analysis weakens dipole-radiation constraints to |b|≲10⁻⁷, with strong degeneracies among dipole strength, chirp mass, and eccentricity.
-
Novel ringdown tests of general relativity with black hole greybody factors
GreyRing model based on greybody factors reproduces numerical relativity ringdown signals with mismatches of order 10^{-6} and enables a new post-merger consistency test of general relativity applied to GW250114.
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Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy
A hierarchical analysis of 69 GWTC-3 black-hole merger candidates yields an effective compactness consistent with C=0.5 and no evidence for a low-compactness exotic population.
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A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences
F-statistic framework analytically maximizes over distance and polarization to enable faster Bayesian inference of compact binary coalescences with a new evidence formulation that matches full frequency-domain results at lower cost.
-
Parameter inference of millilensed gravitational waves using neural spline flows
Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.
-
GW231123: False Massive Graviton Signatures from Unmodeled Point-Mass Lensing
Unmodeled point-mass lensing produces a spurious nonzero graviton mass posterior in GW231123 that vanishes when lensing is included in the analysis.
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Prospect for Detection of Strongly Lensed Multi-messenger Signals of Binary Neutron Star Mergers
Future CE+ET detectors may detect lensed BNS kilonovae at ~0.5/yr via pointed follow-up of known galaxy lenses, while lensed sGRBs and afterglows remain rare or undetectable with current-generation facilities.
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A parametric signal plus noise inference framework for short duration non-Gaussian noise transients
Bilby-antiglitch jointly models astrophysical signals and quasi-physical glitches to recover true source properties from simulated gravitational wave data contaminated by loud non-Gaussian transients.
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Artifact-Conditioned Interval Diagnostics for Flow-Matching Neural Posterior Estimation in a Controlled Gravitational-Wave Benchmark
In a controlled binary-black-hole benchmark, soft learned artifact-aware interval rescaling (LAIR) reduces marginal calibration error for frequency masks from 0.1195 to 0.0672 but is not uniformly better than raw intervals and is positioned as a diagnostic rather than a replacement for full posterio
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Biased parameter inference of eccentric, spin-precessing binary black holes
Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.
-
Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
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Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data
Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.
-
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
GWTC-2.1 adds eight new high-significance compact binary coalescence events to the prior catalog, extending the observed black hole mass range and including candidates inside the pair-instability mass gap.
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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.