A normalising flow trained on real Blip glitches provides a data-informed prior that, used jointly with the signal model in Bilby, removes glitches and reduces bias in gravitational-wave parameter estimates.
This was done both for glitches present in the training data (O1) and for unseen glitches from the O2, O3a, and O3b runs
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Joint inference for gravitational wave signals and glitches using a data-informed glitch model
A normalising flow trained on real Blip glitches provides a data-informed prior that, used jointly with the signal model in Bilby, removes glitches and reduces bias in gravitational-wave parameter estimates.