Ratio-Filter Dechirping converts gravitational-wave matched filtering from a memory-bound FFT into a cache-efficient FIR convolution, delivering a measured 8x speedup in the core loop.
hub
Relative Binning and Fast Likelihood Evaluation for Gravitational Wave Parameter Estimation
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
abstract
We present a method to accelerate the evaluation of the likelihood in gravitational wave parameter estimation. Parameter estimation codes compute likelihoods of similar waveforms, whose phases and amplitudes differ smoothly with frequency. We exploit this by precomputing frequency-binned overlaps of the best-fit waveform with the data. We show how these summary data can be used to approximate the likelihood of any waveform that is sufficiently probable within the required accuracy. We demonstrate that $\simeq 60$ bins suffice to accurately compute likelihoods for strain data at a sampling rate of $4096\,$Hz and duration of $T=2048\,$s around the binary neutron star merger GW170817. Relative binning speeds up parameter estimation for frequency domain waveform models by a factor of $\sim 10^4$ compared to naive matched filtering and $\sim 10$ compared to reduced order quadrature.
hub tools
citation-role summary
method 3
background 1
citation-polarity summary
representative citing papers
Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with ground-informed priors.
Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.
GPU-accelerated nested sampling on GW170817 demonstrates that switching to a uniform-in-dL prior shifts the H0 tail and median far more than post-hoc reweighting captures, due to an under-sampled (dL, iota) bimodality.
Simulations show a 40-50 solar-mass black-hole cutoff is not guaranteed to be confidently recovered from GWTC-4-like catalogs, spurious detections are unlikely, and O4 data would reduce cutoff-mass uncertainty by at least 20 percent while yielding only a lower bound on the carbon-alpha reaction rate
Forecasts that golden and silver dark sirens with HETDEX VIRUS follow-up can constrain H0 to a few percent using one year of LIGO-A# observations for z < 0.2 events.
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
Bilby-antiglitch jointly models astrophysical signals and quasi-physical glitches to recover true source properties from simulated gravitational wave data contaminated by loud non-Gaussian transients.
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
Relative binning accelerates TIGER parameterized GR tests by factors of 10-100 while recovering unbiased posteriors on simulated signals and real events like GW150914.
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
citing papers explorer
-
Constraining Dipole Radiation with Multiband Gravitational Waves from Eccentric Binary Black Holes
Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with ground-informed priors.
-
labrador: A domain-optimized machine-learning tool for gravitational wave inference
Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.
-
Pre-localization of Massive Black Hole Binaries in the Millihertz Band
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.