Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Khan, Sebastian; Green, Rhys

doi:10.48550/arxiv.2008.12932

Cited by 4 publications

(4 citation statements)

References 72 publications

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“…Research and development in deep learning is moving at an incredible pace [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Specific milestones in the development of AI tools for gravitational wave astrophysics include the construction of neural networks that describe the same 4-D signal manifold of established gravitational wave detection pipelines, i.e., the masses of the binary components and the z-component of the 3-D spin vector:…”

Section: Introductionmentioning

confidence: 99%

Accelerated, scalable and reproducible AI-driven gravitational wave detection

et al. 2021

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Finding new ways to use artificial intelligence (AI) to accelerate the analysis of gravitational wave data, and ensuring the developed models are easily reusable promises to unlock new opportunities in multi-messenger astrophysics (MMA), and to enable wider use, rigorous validation, and sharing of developed models by the community. In this work, we demonstrate how connecting recently deployed DOE and NSF-sponsored cyberinfrastructure allows for new ways to publish models, and to subsequently deploy these models into applications using computing platforms ranging from laptops to high performance computing clusters. We develop a workflow that connects the Data and Learning Hub for Science (DLHub), a repository for publishing machine learning models, with the Hardware Accelerated Learning (HAL) deep learning computing cluster, using funcX as a universal distributed computing service. We then use this workflow to search for binary black hole gravitational wave signals in open source advanced LIGO data. We find that using this workflow, an ensemble of four openly available deep learning models can be run on HAL and process the entire month of August 2017 of advanced LIGO data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset, and reporting no misclassifications. This approach, which combines advances in AI, distributed computing, and scientific data infrastructure opens new pathways to conduct reproducible, accelerated, data-driven gravitational wave detection.

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Section: Introductionmentioning

confidence: 99%

Accelerated, scalable and reproducible AI-driven gravitational wave detection

et al. 2021

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“…Deep learning has been applied to the detection of neutron star mergers [68][69][70], forecasting of neutron stars inspirals and neutron star-black hole mergers [71,72], continuous wave sources [73][74][75], signals with complex morphology [61], and to accelerate waveform production [76,77]. The rapid progress and maturity that these algorithms have achieved within just three years, at the time of writing this book, suggest that production-scale deep learning methods are on an accelerated track to become an integral part of gravitational wave discovery [78,79].…”

Section: Deep Learning For Gravitational Wave Data Analysismentioning

confidence: 99%

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Huerta,

Zhao

2021

Preprint

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We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

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“…Many studies have focused on speeding parameter estimation of the source parameters of the signals with various techniques, such as deep learning (George & Huerta 2018), variational autoencoders (Gabbard et al 2019) and autoregressive neural flows (Green et al 2020). Other work has focused on combining detection and parameter estimation with deep neural networks (Fan et al 2019) as well as using neural networks to rapidly generate surrogate waveforms (Khan & Green 2020;Chua et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Density estimation with Gaussian processes for gravitational-wave posteriors

D'Emilio,

Green,

Raymond

2021

Preprint

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The properties of black-hole and neutron-star binaries are extracted from gravitational-wave signals using Bayesian inference. This involves evaluating a multidimensional posterior probability function with stochastic sampling. The marginal probability density distributions from which the samples are drawn are usually interpolated with kernel density estimators. Since most post-processing analysis within the field is based on these parameter estimation products, interpolation accuracy of the marginals is essential. In this work, we propose a new method combining histograms and Gaussian Processes as an alternative technique to fit arbitrary combinations of samples from the source parameters. This method comes with several advantages such as flexible interpolation of non-Gaussian correlations, Bayesian estimate of uncertainty, and efficient re-sampling with Hamiltonian Monte Carlo.

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Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Cited by 4 publications

References 72 publications

Accelerated, scalable and reproducible AI-driven gravitational wave detection

Accelerated, scalable and reproducible AI-driven gravitational wave detection

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Density estimation with Gaussian processes for gravitational-wave posteriors

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