# Generalised gravitational wave burst generation with generative adversarial networks

@article{McGinn2021GeneralisedGW, title={Generalised gravitational wave burst generation with generative adversarial networks}, author={J McGinn and Chris Messenger and M J Williams and Ik Siong Heng}, journal={Classical and Quantum Gravity}, year={2021}, volume={38} }

We introduce the use of conditional generative adversarial networks (CGANs) for generalised gravitational wave (GW) burst generation in the time domain. Generative adversarial networks are generative machine learning models that produce new data based on the features of the training data set. We condition the network on five classes of time-series signals that are often used to characterise GW burst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binary black hole…

## 6 Citations

### DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics

- Computer ScienceArXiv
- 2022

It is shown that discriminating on derivatives can stabilize the learning of GAN components on 1D continuous signals during their training phase, which results in smoother generated signals that are less distinguishable from real samples and better capture the distributions of the training data.

### Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation

- Computer ScienceSSRN Electronic Journal
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A GANs-based algorithm is proposed that allows the replication of multivariate data representing several properties of a set of stocks, which differs from examples in the financial literature, which are mainly focused on the reproduction of temporal asset price scenarios.

### Convolutional neural network for gravitational-wave early alert: Going down in frequency

- Computer Science, PhysicsPhysical Review D
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The latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars is presented, it is shown that the network performs almost as well in non-Gaussian noise as in Gaussian noise: the method is robust w.r.t. glitches and artifacts present in real noise.

### Simulating transient noise bursts in LIGO with generative adversarial networks

- PhysicsPhysical Review D
- 2022

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches,…

### Machine learning algorithm for minute-long burst searches

- Physics, Computer SciencePhysical Review D
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It is shown that the neural network used in this work can reach a pixel-wise detection despite trained with minimal assumptions, while being able to retrieve both astrophysical signals and noise transients originating from instrumental coupling within the detectors.

### Source-agnostic gravitational-wave detection with recurrent autoencoders

- Computer ScienceMach. Learn. Sci. Technol.
- 2022

An application of anomaly detection techniques based on deep recurrent autoencoders (AEs) to the problem of detecting gravitational wave signals in laser interferometers and the recurrent AE outperforms other AEs based on different architectures.

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