Pith. sign in

REVIEW 9 cited by

Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions

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

arxiv 2104.07813 v2 pith:IOHYFZDT submitted 2021-04-15 gr-qc astro-ph.HEastro-ph.IM

classification gr-qcastro-ph.HEastro-ph.IM
keywords binaryestimationparametertechniquewaveformscoalescencecompactcost
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Bayesian parameter estimation of gravitational waves from compact binary coalescence (CBC) typically requires more than millions of evaluations of computationally expensive template waveforms. We propose a technique to reduce the cost of waveform generation by exploiting the chirping behavior of CBC signal. Our technique does not require waveforms at all frequencies in the frequency range used in the analysis, and does not suffer from the fixed cost due to the upsampling of waveforms. Our technique speeds up the parameter estimation of typical binary neutron star signal by a factor of $\mathcal{O}(10)$ for the low-frequency cutoff of $20\,\mathrm{Hz}$, and $\mathcal{O}(10^2)$ for $5\,\mathrm{Hz}$. It does not require any offline preparations or accurate estimates of source parameters provided by detection pipelines.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects

    gr-qc 2026-07 conditional novelty 7.0 of 10

    A single RJMCMC run can rank BBH, NSBH, and BNS waveform models and deliver the favored model's parameter posteriors, validated on injections and two real GW events.

  2. Fast gravitational waveform models for quasi-circular coalescences of neutron star--black hole binaries

    gr-qc 2026-06 unverdicted novelty 7.0 of 10

    Presents new NSBH waveform models IMRPhenomXHM_NSBH, SEOBNRv5HM_ROM_NRTidalv3_NSBH, and IMRPhenomXPHM_NSBH incorporating higher modes and tidal effects via NRTidalv3 extensions, validated against NR simulations and ap...

  3. Binary neutron stars in the next-generation era: Multi-messenger detection prospects and constraints on the equation of state, mass distribution, and cosmology

    astro-ph.HE 2026-07 conditional novelty 6.0 of 10

    With ET (and ET+CE), mock multi-messenger BNS catalogues yield ~40–500 EM counterparts per year and, under ideal recovery, constrain R1.4 to ~0.2 km and H0 to ~1 km s−1 Mpc−1.

  4. Ab Initio Real-Time Gravitational-Wave Parameter Estimation

    gr-qc 2026-07 accept novelty 6.0 of 10

    Slice-within-Gibbs nested sampling on modern GPUs delivers well-calibrated BNS parameter estimation in ~12 minutes uncompressed and ~89 seconds with heterodyning, from cold priors.

  5. Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

    gr-qc 2026-07 accept novelty 6.0 of 10

    A piecewise MLP surrogate emulates NRSur7dq4 over its full domain at NR-faithful accuracy with ~1 ms GPU latency and a fully differentiable JAX likelihood pipeline.

  6. nmma: An extended Bayesian framework for Nuclear Multimessenger Astronomy in the Era of Next-Generation Detectors

    astro-ph.IM 2026-07 accept novelty 6.0 of 10

    nmma now jointly samples nuclear EoS parameters with GW and EM data via TOV emulators and Fiesta surrogates, delivering 20–60× speedups and future H0–nuclear constraints.

  7. The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference

    gr-qc 2026-01 conditional novelty 6.0 of 10

    SHARPy uses Sequential Monte Carlo with a No-U-Turn sampler in JAX to estimate gravitational-wave posteriors and evidence for binary black holes in about ten minutes.

  8. Flexible Gravitational-Wave Parameter Estimation with Transformers

    gr-qc 2025-12 conditional novelty 6.0 of 10

    Dingo-T1 is one transformer model that adapts at inference to arbitrary detector subsets and frequency cuts for gravitational-wave parameter estimation.

  9. PhenomXPNR: An improved gravitational wave model linking precessing inspirals and NR-calibrated merger-ringdown

    gr-qc 2025-07 conditional novelty 4.0 of 10

    PhenomXPNR is a fast frequency-domain gravitational-wave template for spinning black-hole mergers that combines post-Newtonian inspiral precession with numerical-relativity-calibrated merger and ringdown.

Pith tools