Aframe neural network achieves matched-filter sensitivity for binary neutron star GW searches at lower computational cost using heterodyning and a single GPU.
Marxet al., Phys
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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.
F-statistic framework analytically maximizes over distance and polarization to enable faster Bayesian inference of compact binary coalescences with a new evidence formulation that matches full frequency-domain results at lower cost.
Auto-encoder approximates SEOBNRv4 waveforms for four-parameter aligned-spin binaries, delivering 4 orders of magnitude speedup at median mismatch of 10^{-2}.
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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Beyond FINDCHIRP: Breaking the memory wall and optimal FFTs for Gravitational-Wave Matched-Filter Searches with Ratio-Filter Dechirping
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.
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A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences
F-statistic framework analytically maximizes over distance and polarization to enable faster Bayesian inference of compact binary coalescences with a new evidence formulation that matches full frequency-domain results at lower cost.
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Auto-encoder model for faster generation of effective one-body gravitational waveform approximations
Auto-encoder approximates SEOBNRv4 waveforms for four-parameter aligned-spin binaries, delivering 4 orders of magnitude speedup at median mismatch of 10^{-2}.