Image-based deep learning for classification of noise transients in gravitational wave detectors
Problem
Glitches (transient noise events) degrade data quality in gravitational wave detectors like Advanced LIGO and Virgo and need automatic classification.
Why it matters
Detector characterization directly affects the sensitivity and discovery capability of gravitational wave astronomy.
What the AI did
Built a deep learning pipeline using convolutional neural networks to classify glitches from their time-frequency images.
Results
not stated in source
Impact
Evidence shows a working classification pipeline was evaluated, but accuracy figures are cut off in the abstract.
Record
| Venue | Classical and Quantum Gravity |
|---|---|
| Published | 2018 |
| Work type | article |
| Authors | Massimiliano Razzano, E. Cuoco |
| Citations | 96 (OpenAlex, at import time) |
| References | 47 |
| Model or method | Convolutional neural networks |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1088/1361-6382/aab793 |
| Open access | bronze |
| Keywords | deep learning, CNN, gravitational waves, glitch classification, detector characterization |
Importance rating
significant — thousands to millions affected
Glitch classification supports detector characterization for Advanced LIGO/Virgo, underpinning sensitivity of gravitational wave astronomy, a foundational scientific field; no scale quantified.
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