Aframe neural network achieves matched-filter sensitivity for binary neutron star GW searches at lower computational cost using heterodyning and a single GPU.
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Mode-by-mode filtering of higher-order modes enables low-latency marginalization over mode information in NSBH gravitational-wave signals, tightening constraints on distance, inclination, and secondary mass.
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.
PyCBC Live for O4 adds time-dependent background modeling, early warning search, and improved autogating, delivering sensitivity gains of 1.7-2.3x for coincident searches in mock data challenges.
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
KAGRA enhances sky localization of binary neutron star mergers in the LVK network via added baselines, with measurable gains at current sensitivity and larger improvements as range reaches ~30 Mpc.
citing papers explorer
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AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity
Aframe neural network achieves matched-filter sensitivity for binary neutron star GW searches at lower computational cost using heterodyning and a single GPU.
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Improving low-latency multi-messenger follow-up of neutron star-black hole mergers with mode-by-mode filtering
Mode-by-mode filtering of higher-order modes enables low-latency marginalization over mode information in NSBH gravitational-wave signals, tightening constraints on distance, inclination, and secondary mass.
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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.
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PyCBC Live Search for Compact Binary Mergers in Advanced LIGO and Virgo's Fourth Observing Run
PyCBC Live for O4 adds time-dependent background modeling, early warning search, and improved autogating, delivering sensitivity gains of 1.7-2.3x for coincident searches in mock data challenges.
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Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
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Investigating the effect of sensitivity of KAGRA on sky localization of gravitational-wave sources from compact binary coalescences
KAGRA enhances sky localization of binary neutron star mergers in the LVK network via added baselines, with measurable gains at current sensitivity and larger improvements as range reaches ~30 Mpc.