A naukri.com initiative
Arxiv
3d
362
Image Credit: Arxiv
DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
- Gravitational wave detectors like LIGO, Virgo, and KAGRA are sensitive to signals from distant astrophysical events but can be affected by background noise, including glitches.
- DeepExtractor is a deep learning framework introduced to reconstruct signals and glitches in gravitational wave data, surpassing interferometer noise levels.
- This model is designed to capture the noise distribution of GW detectors assuming Gaussian and stationary noise over short time intervals, aiming to separate signal or glitch from noise.
- DeepExtractor was tested through experiments including simulated glitches in detector noise, comparison with the BayesWave algorithm, and analyzing real data from the Gravity Spy dataset for glitch subtraction in LIGO strain data.
- The model performed well in reconstructing simulated glitches with a median mismatch of only 0.9%, outperforming other deep learning baselines.
- DeepExtractor also excelled in glitch recovery compared to BayesWave, offering a significant speedup by reconstructing one glitch sample in about 0.1 seconds on a CPU, much faster than BayesWave's processing time of about an hour per glitch.
Read Full Article
21 Likes
For uninterrupted reading, download the app