Gravitational wave parameter estimation with compressed likelihood evaluations

Abstract

One of the main bottlenecks in gravitational wave (GW) astronomy is the high cost of performing parameter estimation and GW searches on the fly. We propose a novel technique based on Reduced Order Quadratures (ROQs), an application and data-specific quadrature rule, to perform fast and accurate likelihood evaluations. These are the dominant cost in Markov chain Monte Carlo (MCMC) algorithms, which are widely employed in parameter estimation studies, and so ROQs offer a new way to accelerate GW parameter estimation. We illustrate our approach using a four dimensional GW burst model embedded in noise. We build an ROQ for this model, and perform four dimensional MCMC searches with both the standard and ROQs quadrature rules, showing that, for this model, the ROQ approach is around 25 times faster than the standard approach with essentially no loss of accuracy. The speed-up from using ROQs is expected to increase for more complex GW signal models and therefore has significant potential to accelerate parameter estimation of GW sources such as compact binary coalescences.

18 pages, 12 figures (2 updated) and 2 tables. Minor edits based on referee report

References in corpus (5)

• Singular value decomposition applied to compact binary coalescence gravitational-wave signals
• Testing Chern-Simons Modified Gravity with Gravitational-Wave Detections of Extreme-Mass-Ratio Binaries
• An algorithm for detection of extreme mass ratio inspirals in LISA data
• The Final Merger of Black-Hole Binaries
• Statistical constraints on binary black hole inspiral dynamics

Cited by in corpus (55)

• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Black holes, gravitational waves and fundamental physics: a roadmap
• Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
• Fast prediction and evaluation of gravitational waveforms using surrogate models
• FastEMRIWaveforms: New tools for millihertz gravitational-wave data analysis
• Frequency domain reduced order models for gravitational waves from aligned-spin compact binaries
• First search for gravitational waves from known pulsars with Advanced LIGO
• Fast and Accurate Inference on Gravitational Waves from Precessing Compact Binaries
• Frequency domain reduced order model of aligned-spin effective-one-body waveforms with generic mass-ratios and spins
• Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveform templates for LISA data analysis
• Parameter estimation for binary neutron-star coalescences with realistic noise during the Advanced LIGO era
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• A novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences
• Accelerated gravitational-wave parameter estimation with reduced order modeling
• The Science of the Einstein Telescope
• Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression
• Reduced-order modeling with artificial neurons for gravitational-wave inference
• Higher order gravitational-wave modes with likelihood reweighting
• Bilby-MCMC: An MCMC sampler for gravitational-wave inference
• Parameterized tests of the strong-field dynamics of general relativity using gravitational wave signals from coalescing binary black holes: Fast likelihood calculations and sensitivity of the method
• Effective-one-body waveforms for binary neutron stars using surrogate models
• A hybrid post-Newtonian -- effective-one-body scheme for spin-precessing compact-binary waveforms
• Accelerating gravitational wave parameter estimation with multi-band template interpolation
• Parameter estimation on gravitational waves from neutron-star binaries with spinning components
• Accelerating parameter inference with graphics processing units
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Accelerating the evaluation of inspiral-merger-ringdown waveforms with adapted grids
• Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks
• Towards a framework for testing general relativity with extreme-mass-ratio-inspiral observations
• Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning
• Reduced Order and Surrogate Models for Gravitational Waves
• Near Real-Time Gravitational Wave Data Analysis of the Massive Black Hole Binary with TianQin
• A sparse representation of gravitational waves from precessing compact binaries
• PyROQ: a Python-based Reduced Order Quadrature Building Code for Fast Gravitational Wave Inference
• Extended reduced-order surrogate models for scalar-tensor gravity in the strong field and applications to binary pulsars and gravitational waves
• An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models
• Efficient Reduced Order Quadrature Construction Algorithms for Fast Gravitational Wave Inference
• Swift sky localization of gravitational waves using deep learning seeded importance sampling
• Prompt sky localization of compact binary sources using a meshfree approximation
• Rapidly evaluating the compact binary likelihood function via interpolation
• Rapid gravitational wave parameter estimation with a single spin: Systematic uncertainties in parameter estimation with the SpinTaylorF2 approximation
• Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
• Phenomenological model of gravitational self-force enhanced tides in inspiralling binary neutron stars
• A fully precessing higher-mode surrogate model of effective-one-body waveforms
• Pinpointing coalescing binary neutron star sources with the IGWN, including LIGO-Aundha
• Fast prediction and evaluation of eccentric inspirals using reduced-order models
• Inferring Binary Properties from Gravitational Wave Signals
• Fast and faithful interpolation of numerical relativity surrogate waveforms using meshfree approximation
• Optimized localization for gravitational-waves from merging binaries
• Likelihood smoothing using gravitational wave surrogate models
• Characterizing Compact-object Binaries in the Lower Mass Gap with Gravitational Waves
• Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA
• Tunable compression of template banks for fast gravitational-wave detection and localisation
• Significant challenges for astrophysical inference with next-generation gravitational-wave observatories
• Reconsidering the consistent use of precessing, higher order multipole models for gravitational wave analyses