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Licence to Bin: Accurate and Scalable Inference for Binary Neutron Stars in Next-Generation Gravitational-Wave Detectors
High Energy Astrophysical Phenomena · Submitted 12 Jun 2026
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim These results demonstrate that reduced-order methods can make next-generation binary neutron-star inference computationally feasible.
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The abstract, sentence by sentence
Next-generation gravitational-wave observatories will observe binary neutron-star mergers with much higher signal-to-noise ratios, over much longer durations and across broader frequency bands than current detectors. These long-duration signals present a major computational challenge for Bayesian parameter estimation. Reduced-order quadrature is a promising approach for accelerating inference, but in this regime its standard construction encounters severe memory and accuracy limitations. We present a practical reduced-order quadrature construction for long binary neutron-star signals with time-dependent detector response and full effects of the observatories' free-spectral range. Our approach combines improved adaptive frequency sampling, disk-backed streaming, and subbanded reduced-order quadrature construction, enabling efficient and accurate reduced-order models for signals that were previously intractable. We demonstrate for the first time reduced-order Bayesian inference on an approximately 2 h binary neutron-star signal extending down to 5 Hz and with signal-to-noise ratio 2090. We show the resulting reduced-order quadrature remains sufficiently accurate for practical inference. The full analysis is carried out in about 48 h using 128 CPU cores. We also find that, when time-dependent detector-response effects are included, a single Cosmic Explorer detector can localize such a signal to a 90% credible sky area of approximately $41~\mathrm{deg}^2$, with important implications for multimessenger astronomy and cosmology. These results demonstrate that reduced-order methods can make next-generation binary neutron-star inference computationally feasible.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Next-generation gravitational-wave observatories will observe binary neutron-star mergers with much higher signal-to-noise ratios, over much longer durations and across broader frequency bands than current detectors. | background 1.00 | 0.08 | 0.02 | asserted 0.92 |
| s1 | These long-duration signals present a major computational challenge for Bayesian parameter estimation. | background 0.96 | 0.07 | 0.03 | asserted 1.00 |
| s2 | Reduced-order quadrature is a promising approach for accelerating inference, but in this regime its standard construction encounters severe memory and accuracy limitations. | gap 0.55 | 0.11 | 0.03 | asserted 0.84 |
| s3 | We present a practical reduced-order quadrature construction for long binary neutron-star signals with time-dependent detector response and full effects of the observatories' free-spectral range. | contribution 0.94 | 0.94 | 0.13 | asserted 1.00 |
| s4 | Our approach combines improved adaptive frequency sampling, disk-backed streaming, and subbanded reduced-order quadrature construction, enabling efficient and accurate reduced-order models for signals that were previously intractable. | method 0.87 | 0.93 | 0.16 | asserted 1.00 |
| s5 | We demonstrate for the first time reduced-order Bayesian inference on an approximately 2 h binary neutron-star signal extending down to 5 Hz and with signal-to-noise ratio 2090. | contribution 0.56 | 0.97 | 0.15 | asserted 1.00 |
| s6 | We show the resulting reduced-order quadrature remains sufficiently accurate for practical inference. | result 1.00 | 0.94 | 0.14 | asserted 0.97 |
| s7 | The full analysis is carried out in about 48 h using 128 CPU cores. | result 0.85 | 0.95 | 0.05 | asserted 1.00 |
| s8 | We also find that, when time-dependent detector-response effects are included, a single Cosmic Explorer detector can localize such a signal to a 90% credible sky area of approximately $41~\mathrm{deg}^2$, with important implications for multimessenger astronomy and cosmology. | result 0.89 | 0.94 | 0.11 | asserted 1.00 |
| s9 | These results demonstrate that reduced-order methods can make next-generation binary neutron-star inference computationally feasible. | interpretation 0.99 | 0.76 | 0.86 | asserted 1.00 |
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Original abstract
Next-generation gravitational-wave observatories will observe binary neutron-star mergers with much higher signal-to-noise ratios, over much longer durations and across broader frequency bands than current detectors. These long-duration signals present a major computational challenge for Bayesian parameter estimation. Reduced-order quadrature is a promising approach for accelerating inference, but in this regime its standard construction encounters severe memory and accuracy limitations. We present a practical reduced-order quadrature construction for long binary neutron-star signals with time-dependent detector response and full effects of the observatories' free-spectral range. Our approach combines improved adaptive frequency sampling, disk-backed streaming, and subbanded reduced-order quadrature construction, enabling efficient and accurate reduced-order models for signals that were previously intractable. We demonstrate for the first time reduced-order Bayesian inference on an approximately 2 h binary neutron-star signal extending down to 5 Hz and with signal-to-noise ratio 2090. We show the resulting reduced-order quadrature remains sufficiently accurate for practical inference. The full analysis is carried out in about 48 h using 128 CPU cores. We also find that, when time-dependent detector-response effects are included, a single Cosmic Explorer detector can localize such a signal to a 90% credible sky area of approximately $41~\mathrm{deg}^2$, with important implications for multimessenger astronomy and cosmology. These results demonstrate that reduced-order methods can make next-generation binary neutron-star inference computationally feasible.
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References
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1 arXiv paper cite this in 1 sentence
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Cites as background 4 papers
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Considerable effort has therefore been devoted to accelerating parameter estimation [ref] .
Relative Binning and Fast Likelihood Evaluation for Gravitational Wave Parameter Estimation · Introduction · 0.90
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Considerable effort has therefore been devoted to accelerating parameter estimation [ref] .
Accelerated parameter estimation in Bilby with relative binning · Introduction · 0.90
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Considerable effort has therefore been devoted to accelerating parameter estimation [ref] .
Real-time gravitational-wave inference for binary neutron stars using machine learning · Introduction · 0.90
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