B. P. Abbott Created 2026-10-18 Updated 2026-11-05
B. P. Abbott coauthored the collaborative analyses of GW150914. Fitting general relativity waveforms to LIGO data constrained the properties of the merging binary black hole.[ref]
Properties of the Binary Black Hole Merger GW150914 by B. P. Abbott and others. Author list and abstract: coherent analysis across the detector network using compact-binary waveform models and inferred source parameters.
A companion study converted detector control-loop signals into gravitational wave strain, with an optomechanical response model and independent checks of its measurement uncertainty.[ref]Calibration of the Advanced LIGO detectors for the discovery of the binary black-hole merger GW150914 by B. P. Abbott and others. Author list and abstract: time-domain calibration filters, optomechanical/feedback response and validation with radiation-pressure actuation.
Inferring a binary from a detector network 2026-10-18
For detector , model the calibrated data as , with a shared source parameter vector , separate detector responses, propagation delays and noise . Under an independent stationary Gaussian process approximation with one-sided power spectral density , define the noise-weighted inner product and likelihood functionStrain power spectral density has units ; the displayed inner product is dimensionless when the Fourier transform integrates over seconds. A matched filter rewards agreement over the full evolving wave phase, weighting frequency bands by their noise. One conspicuous peak is insufficient: a candidate should also have the right distribution of signal across frequency bands and compatible projections in the network.
The parameter paper used coherent Bayesian inference, numerical relativity-informed inspiral–merger–ringdown models and explicit calibration uncertainties. It found a strong distance–inclination correlation; source-frame masses additionally require converting the detector-frame masses by a cosmological redshift.[ref]
Properties of the Binary Black Hole Merger GW150914 by B. P. Abbott and others. II Parameter estimation, equations (1)–(5), II.A waveform models; III Results, p. 7, Fig. 2
Conceptually, multiply the likelihood function by a prior distribution to obtain a posterior distribution. Changes in waveform families, residual structure after subtraction, and checks with a generic transient reconstruction test parts of the modeling assumptions. Detection significance and parameter uncertainty answer different questions.