Rapid and Robust Parameter Inference for Binary Mergers

Abstract

The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.

13 pages, 11 figures

References in corpus (11)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise
• The BayesWave analysis pipeline in the era of gravitational wave observations
• Machine-learning non-stationary noise out of gravitational wave detectors
• Realtime search for compact binary mergers in Advanced LIGO and Virgo's third observing run using PyCBC Live
• A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
• Accelerating parameter inference with graphics processing units
• Modeling compact binary signals and instrumental glitches in gravitational wave data
• Black Hole Hunting with LISA

Cited by in corpus (22)

• Real-time gravitational-wave science with neural posterior estimation
• Parameter estimation with gravitational waves
• Heterodyned Likelihood for Rapid Gravitational Wave Parameter Inference
• Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions
• Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• Transient glitch mitigation in Advanced LIGO data with $\text{glitschen}$
• The Issues of Mismodelling Gravitational-Wave Data for Parameter Estimation
• Accelerating Multi-Model Bayesian Inference, Model Selection and Systematic Studies for Gravitational Wave Astronomy
• Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
• Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
• Low Latency Detection of Massive Black Hole Binaries
• Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization
• Neural post-Einsteinian framework for efficient theory-agnostic tests of general relativity with gravitational waves
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Semianalytical Approach for Sky Localization of Gravitational Waves
• Inferring Binary Properties from Gravitational Wave Signals
• Optimized localization for gravitational-waves from merging binaries
• Application of a new transient-noise analysis tool for an unmodeled gravitational-wave search pipeline
• labrador: A domain-optimized machine-learning tool for gravitational wave inference
• Probing neutron stars with the full pre-merger and post-merger gravitational wave signal from binary coalescences
• MaxWave: Rapid maximum likelihood wavelet reconstruction of non-Gaussian features in gravitational wave data