Accelerating gravitational wave parameter estimation with multi-band template interpolation
arXiv:1703.02062 · doi:10.1088/1361-6382/aa6d44
Abstract
Parameter estimation on gravitational wave signals from compact binary coalescence (CBC) requires the evaluation of computationally intensive waveform models, typically the bottleneck in the analysis. This cost will increase further as low frequency sensitivity in later second and third generation detectors motivates the use of longer waveforms. We describe a method for accelerating parameter estimation by exploiting the chirping behaviour of the signals to sample the waveform sparsely for portions where the full frequency resolution is not required. We demonstrate that the method can reproduce the original results with a waveform mismatch of , but with a waveform generation cost up to times lower for computationally costly frequency-domain waveforms starting from below 8 Hz.
References in corpus (7)
- Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
- Comparison of post-Newtonian templates for compact binary inspiral signals in gravitational-wave detectors
- Post-1-Newtonian tidal effects in the gravitational waveform from binary inspirals
- Accelerated gravitational-wave parameter estimation with reduced order modeling
- Length requirements for numerical-relativity waveforms
- The Loudest Gravitational Wave Events
- A New Method of Accelerated Bayesian Inference for Comparable Mass Binaries in both Ground and Space-Based Gravitational Wave Astronomy
Cited by in corpus (52)
- Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
- IMRPhenomXHM: A multi-mode frequency-domain model for the gravitational wave signal from non-precessing black-hole binaries
- Setting the cornerstone for the IMRPhenomX family of models for gravitational waves from compact binaries: The dominant harmonic for non-precessing quasi-circular black holes
- Real-time gravitational-wave science with neural posterior estimation
- Phenomenological model for the gravitational-wave signal from precessing binary black holes with two-spin effects
- Including higher order multipoles in gravitational-wave models for precessing binary black holes
- Exploring the Bayesian parameter estimation of binary black holes with LISA
- Parallelized Inference for Gravitational-Wave Astronomy
- Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
- The Science of the Einstein Telescope
- Parameter estimation with gravitational waves
- Effective-one-body waveforms for precessing coalescing compact binaries with post-newtonian Twist
- Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression
- Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions
- Accelerating parameter inference with graphics processing units
- Measurement Accuracy of Inspiraling Eccentric Neutron Star and Black Hole Binaries Using Gravitational Waves
- Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
- Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature
- Searching for cosmological gravitational-wave backgrounds with third-generation detectors in the presence of an astrophysical foreground
- Real-time gravitational-wave inference for binary neutron stars using machine learning
- Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models
- Accelerating the evaluation of inspiral-merger-ringdown waveforms with adapted grids
- Fast, faithful, frequency-domain effective-one-body waveforms for compact binary coalescences
- Multiband Gravitational Wave Parameter Estimation: A Study of Future Detectors
- Regression methods in waveform modeling: a comparative study
- VARAHA: A Fast Non-Markovian sampler for estimating Gravitational-Wave posteriors
- Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning
- Near Real-Time Gravitational Wave Data Analysis of the Massive Black Hole Binary with TianQin
- Particle Swarm Optimization based search for gravitational waves from compact binary coalescences: performance improvements
- PyROQ: a Python-based Reduced Order Quadrature Building Code for Fast Gravitational Wave Inference
- An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models
- Parameter estimation with the current generation of phenomenological waveform models applied to the black hole mergers of GWTC-1
- Assessing gravitational-wave binary black hole candidates with Bayesian odds
- Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
- Scalable data-analysis framework for long-duration gravitational waves from compact binaries using short Fourier transforms
- Phenomenological model of gravitational self-force enhanced tides in inspiralling binary neutron stars
- GPU-accelerated LISA parameter estimation with full time domain response
- Computational Techniques for Parameter Estimation of Gravitational Wave Signals
- Inferring small neutron star spins with neutron star-black hole mergers
- Inferring Binary Properties from Gravitational Wave Signals
- Modeling matter(s) in SEOBNRv5THM: Generating fast and accurate effective-one-body waveforms for spin-aligned binary neutron stars
- Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder
- Accelerated parameter estimation of supermassive black hole binaries in LISA using a meshfree approximation
- Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
- Accelerating gravitational-wave parameterized tests of General Relativity using a multiband decomposition of likelihood
- Flexible Gravitational-Wave Parameter Estimation with Transformers
- Comparing next-generation detector configurations for high-redshift gravitational wave sources with neural posterior estimation
- Probing the peak of star formation with the stochastic background of binary black hole mergers
- Accurate and efficient simulation-based inference for massive black-hole binaries with LISA
- Fast Waveform Generation for Gravitational Waves using Evolutionary Algorithms
- Reconsidering the consistent use of precessing, higher order multipole models for gravitational wave analyses
- Auto-encoder model for faster generation of effective one-body gravitational waveform approximations