A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Hunting for new glitches in LIGO data using community science
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.