A new gravitational wave event reveals a binary black hole merger with total mass 190-265 solar masses, indicating black holes can form via gravitational-wave driven mergers beyond standard stellar channels.
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A saliency-guided wavelet framework extracts transient glitches from gravitational-wave strain data by pre-tagging candidates with UMAP, identifying time-frequency regions on CWT spectrograms, and enabling exact reconstruction via adaptive DWT coefficient masking.
Score-based diffusion models learn the empirical distribution of real LIGO noise to enable unbiased gravitational-wave parameter estimation under only an additivity assumption.
citing papers explorer
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GW231123: a Binary Black Hole Merger with Total Mass 190-265 $M_{\odot}$
A new gravitational wave event reveals a binary black hole merger with total mass 190-265 solar masses, indicating black holes can form via gravitational-wave driven mergers beyond standard stellar channels.
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Wavelet-Based Extraction of Transient Noise in Gravitational-Wave Interferometers using a Saliency-Guided Learning Architecture
A saliency-guided wavelet framework extracts transient glitches from gravitational-wave strain data by pre-tagging candidates with UMAP, identifying time-frequency regions on CWT spectrograms, and enabling exact reconstruction via adaptive DWT coefficient masking.
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Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Score-based diffusion models learn the empirical distribution of real LIGO noise to enable unbiased gravitational-wave parameter estimation under only an additivity assumption.