# Image-based deep learning for classification of noise transients in gravitational wave detectors

arXiv: https://arxiv.org/abs/1803.09933 · HTML: https://mlsanity.com/paper/1803.09933/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 10 of 10)

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- [Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows](https://mlsanity.com/paper/2310.12209/) (2023)
- [Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning](https://mlsanity.com/paper/2203.05086/) (2022)
- [Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks](https://mlsanity.com/paper/2207.11583/) (2022)
- [Flexible Gravitational-Wave Parameter Estimation with Transformers](https://mlsanity.com/paper/2512.02968/) (2025)
- [Dilated convolutional neural network for detecting extreme-mass-ratio inspirals](https://mlsanity.com/paper/2308.16422/) (2023)

## Abstract (arXiv)

The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.