
Physicsinspired deep learning to characterize the signal manifold of quasicircular, spinning, nonprecessing binary black hole mergers
The spin distribution of binary black hole mergers contains key informat...
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Convergence of Artificial Intelligence and High Performance Computing on NSFsupported Cyberinfrastructure
Significant investments to upgrade or construct largescale scientific f...
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Deep Learning for Cardiologistlevel Myocardial Infarction Detection in Electrocardiograms
Heart disease is the leading cause of death worldwide. Amongst patients ...
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Enabling realtime multimessenger astrophysics discoveries with deep learning
Multimessenger astrophysics is a fastgrowing, interdisciplinary field ...
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Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising AutoEncoders
Denoising of time domain data is a crucial task for many applications su...
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Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers
We present the first application of deep learning at scale to do gravita...
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Deep Learning for MultiMessenger Astrophysics: A Gateway for Discovery in the Big Data Era
This report provides an overview of recent work that harnesses the Big D...
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The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective
Gravitational wave observations of eccentric binary black hole mergers w...
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Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey
Large scale astronomical surveys continue to increase their depth and sc...
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Supporting HighPerformance and HighThroughput Computing for Experimental Science
The advent of experimental science facilities, instruments and observato...
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Realtime regression analysis with deep convolutional neural networks
We discuss the development of novel deep learning algorithms to enable r...
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Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Gravitational wave astronomy is a rapidly growing field of modern astrop...
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Deep Learning for Realtime Gravitational Wave Detection and Parameter Estimation with LIGO Data
The recent Nobelprizewinning detections of gravitational waves from me...
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Glitch Classification and Clustering for LIGO with Deep Transfer Learning
The detection of gravitational waves with LIGO and Virgo requires a deta...
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Eccentric, nonspinning, inspiral, Gaussianprocess merger approximant for the detection and characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiralmergerringdown waveform mode...
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ENIGMA: Eccentric, Nonspinning, Inspiral Gaussianprocess Merger Approximant for the characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiralmergerringdown waveform mode...
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Deep Learning for Realtime Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
The recent Nobelprizewinning detections of gravitational waves from me...
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Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and postprocess numerical relativity simulations
Numerical simulations of Einstein's field equations provide unique insig...
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Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
The exquisite sensitivity of the advanced LIGO detectors has enabled the...
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E. A. Huerta
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pioneering the use of Deep Learning to study gravitational waves