3.6k citations · 8.6k across the 25 of their papers we have counts for
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Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
Wei Wei, E. A. Huerta, Mengshen Yun +5
We present the first application of deep learning forecasting for binary neutron stars, neutron star - black hole systems, and binary black hole mergers that span an eccentricity r…
Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
Wei Wei, Asad Khan, E. A. Huerta +2
We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent ne…
Deep learning for gravitational wave forecasting of neutron star mergers
Wei Wei, E. A. Huerta
We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real…
Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks
Zhuo Chen, E. A. Huerta, Joseph Adamo +4
[Abridged] We introduce an improved version of the Eccentric, Non-spinning, Inspiral-Gaussian-process Merger Approximant (ENIGMA) waveform model. We find that this ready-to-use mod…
Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
Asad Khan, E. A. Huerta, Arnav Das
The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form,…
Enabling real-time multi-messenger astrophysics discoveries with deep learning
E. A. Huerta, Gabrielle Allen, Igor Andreoni +57
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that…