Accelerating parameter inference with graphics processing units

Wysocki, D. M.; O’Shaughnessy, R.; Lange, J.; Fang, Yao-Lung L.

doi:10.1103/physrevd.99.084026

Cited by 81 publications

(96 citation statements)

References 41 publications

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“…Additional analyses were performed with the package LALInference [102]. RIFT [103,104] was also used to check consistency of the intrinsic parameters and for corroborating the Bayes factors that are presented below. The power spectral density (PSD) of the noise that enters the likelihood calculation is estimated from the data using BayesWave [105,106].…”

Section: B Methods and Signal Modelsmentioning

confidence: 99%

“…To estimate systematic uncertainties, we test the same hypotheses using multiple model families and multiple codes to calculate log 10 B. Bilby [99][100][101] and LALInference [102] use variants of the nested sampling algorithm [145][146][147][148]. RIFT [103,104] is based on interpolating the marginalized likelihood over a grid covering only the intrinsic source parameters. We consistently find log 10 B ≥ 3 in favor of higher multipoles.…”

Section: A Bayes Factors and Matched-filter Snrmentioning

confidence: 99%

See 1 more Smart Citation

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

Abbott¹,

Abbott²,

Abraham³

et al. 2020

Phys. Rev. D

Self Cite

579

408

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We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO's and Virgo's third observing run. The signal was recorded on April 12, 2019 at 05∶30∶44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: a ∼30 M ⊙ black hole merged with a ∼8 M ⊙ black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed on GW190412 indicates consistency with Einstein's general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs.

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Section: B Methods and Signal Modelsmentioning

confidence: 99%

Section: A Bayes Factors and Matched-filter Snrmentioning

confidence: 99%

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

Abbott¹,

Abbott²,

Abraham³

et al. 2020

Phys. Rev. D

Self Cite

579

408

View full text Add to dashboard Cite

show abstract

“…We note that misidentifying a light BH as a heavy NS will introduce significantly less bias in inferred NS parameters than misidentifying a BH as a light NS [67]. In such scenarios, the use of nonprecessing approximants for parameter estimation could introduce nontrivial biases in the inferred population properties, including inferences on the NS equation of state [14,16,157].…”

Section: Precessing Nonprecessingmentioning

confidence: 97%

Measuring precession in asymmetric compact binaries

Pratten

Schmidt

Buscicchio

et al. 2020

Phys. Rev. Research

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Gravitational-wave observations of merging compact binaries hold the key to precision measurements of the objects' masses and spins. General relativistic precession, caused by spins misaligned with the orbital angular momentum, is considered a crucial tracer for determining the binary's formation history and environment and it also improves mass estimates; its measurement is therefore of particular interest with wide-ranging implications. Precession leaves a characteristic signature in the emitted gravitational-wave signal that is even more pronounced in binaries with highly unequal masses. The recent observations of GW190412 and GW190814 have confirmed the existence of such asymmetric compact binaries. Here we perform a systematic study to assess the confidence in measuring precession in gravitational-wave observations of high-mass-ratio binaries and our ability to measure the mass of the lighter companion in neutron-star-black-hole-type systems. Using Bayesian model selection, we show that precession can be decisively identified for low-mass binaries with mass ratios as low as 1:3 and mildly precessing spins with magnitudes 0.4, even in the presence of systematic waveform errors.

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“…In population inference, we are interested in measuring hyper-parameters, Λ, describing a population of binaries (e.g., minimum/maximum black hole mass) rather than the parameters, θ, of each of the individual binaries. The population properties are often described by either phenomenological models (e.g., [32][33][34][35][36][37][38][39][40][41]) or by the results of detailed physical simulations, e.g., population synthesis or N-body dynamical simulations (e.g., [42][43][44][45][46][47][48][49][50][51][52]). In this work, we use the former for examples.…”

Section: Population Accelerationmentioning

confidence: 99%

Parallelized inference for gravitational-wave astronomy

et al. 2019

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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 ∼ 50× for compact binary coalescence gravitational waveform generation and likelihood evaluation, and more than 100× 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.

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Accelerating parameter inference with graphics processing units

Cited by 81 publications

References 41 publications

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

Measuring precession in asymmetric compact binaries

Parallelized inference for gravitational-wave astronomy

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