REVIEW 9 cited by
Parallelized Inference for Gravitational-Wave Astronomy
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Parallelized Inference for Gravitational-Wave Astronomy
read the original abstract
Bayesian inference is the workhorse of gravitational-wave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact binaries. The science enabled by these inferences comes with a computational cost that can limit the questions we are able to answer. This cost is expected to grow. As detectors improve, the detection rate will go up, allowing less time to analyze each event. Improvement in low-frequency sensitivity will yield longer signals, increasing the number of computations per event. The growing number of entries in the transient catalog will drive up the cost of population studies. While Bayesian inference calculations are not entirely parallelizable, key components are embarrassingly parallel: calculating the gravitational waveform and evaluating the likelihood function. Graphical processor units (GPUs) are adept at such parallel calculations. We report on progress porting gravitational-wave inference calculations to GPUs. Using a single code - which takes advantage of GPU architecture if it is available - we compare computation times using modern GPUs (NVIDIA P100) and CPUs (Intel Gold 6140). We demonstrate speed-ups of $\sim 50 \times$ for compact binary coalescence gravitational waveform generation and likelihood evaluation and more than $100\times$ for population inference within the lifetime of current detectors. Further improvement is likely with continued development. Our python-based code is publicly available and can be used without familiarity with the parallel computing platform, CUDA.
Forward citations
Cited by 9 Pith papers
-
GW Microlensing: Degeneracy with Unlensed Precessing and Non-Spinning Gravitational-Wave Signals
A CNN trained on Q-transform images separates microlensed from unlensed precessing and non-spinning black-hole merger signals with 80–95% accuracy in simulated noise.
-
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.
-
Fortifying gravitational-wave population inference with normalizing flows
Representing each gravitational-wave event's posterior with a normalizing flow lets analysts generate enough cheap posterior samples to keep the Monte-Carlo variance of population inference below threshold for catalog...
-
Can Eccentric Binary Black Hole Signals Mimic Gravitational-Wave Microlensing?
Eccentric BBH signals can masquerade as wave-optics microlensing in quasicircular analyses, but eccentric recovery templates break the degeneracy.
-
Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy
Hierarchical analysis of GWTC-3 events measures effective compactness C_eff = 0.5^{+0.3}_{-0.1} consistent with black holes and limits low-compactness exotic merger rate to <0.7 Gpc^{-3} yr^{-1}.
-
Disentangling spinning and nonspinning binary black hole populations with spin sorting
Spin sorting with the default spin model distinguishes spinning and nonspinning binary black hole populations in simulations and shows real data rule out a fully nonspinning population but allow mixed ones with up to ...
-
Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy
Hierarchical analysis of GWTC-3 yields C_eff = 0.5^{+0.3}_{-0.1} consistent with black holes and limits low-compactness exotic binary merger rate to <0.7 Gpc^{-3} yr^{-1}.
-
An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.