Gravitational Wave Detection
2 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems.
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds
In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability.
Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection
Finally, when we distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, our AI ensemble is capable of processing a decade of gravitational wave data from a three detector network within 3. 5 hours.
Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition
This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem.
DeepSNR: A deep learning foundation for offline gravitational wave detection
In this paper, the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which uses a novel method for generating a signal-to-noise ratio ranking statistic from deep learning classifiers, is introduced, providing the first foundation for the use of deep learning algorithms in discovery-oriented pipelines.
On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks
We show that the proposed method can provide an alternative to transfer learning for the classification of spectrograms using deep networks, i. e. using a high-resolution GAN for data augmentation instead.
A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals
Even if the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature.
Inference-optimized AI and high performance computing for gravitational wave detection at scale
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours.
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
The recurrent autoencoder outperforms other autoencoders based on different architectures.
Generalized Approach to Matched Filtering using Neural Networks
Moreover, we show that the proposed neural network architecture can outperform matched filtering, both with or without knowledge of a prior on the parameter distribution.
