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In this paper we investigate the impact of transient noise artifacts, or glitches, on gravitational- wave inference from ground-based interferometer data, and test how modeling and subtracting these glitches affects the inferred parameters. Due to their time-frequency morphology, broadband glitches cause moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches induce negligible biasing effects, due to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three occurring glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using BayesWave, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching pro- cedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint BayesWave inference. We produce posterior distributions on the parameters of the injected signal before and after subtracting the glitch, and we show that removing the transient noise effectively mitigates bias from broadband glitches. This study provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data.
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Statistically-informed deep learning for gravitational wave parameter estimation
Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, ( m 1 , m 2 ) , and three astrophysical properties of the post-merger compact remnant, namely, the final spin, a f , and the frequency and damping time of the ringdown oscillations of the fundamental ℓ = m = 2 bar mode, ( ω R , ω I ) . Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters ( m 1 , m 2 , a f , ω R , ω I ) of five binary black holes: GW150914 , GW170104 , GW170814 , GW190521 and GW190630 . We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science .
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- Award ID(s):
- 1934757
- PAR ID:
- 10352446
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- 015007
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/2632-2153/ac3843
