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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.
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Publisher Copyright: © Published under licence by IOP Publishing Ltd.; Conference date: 24-27 June 2017
F. Acernese, T. Adams, et al · 2017
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The LIGO Scientific Collaboration, the Virgo Collaboration, et al
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M. Zevin, S. Coughlin, S. Bahaadini, E. Besler, N. Rohani, S. Allen, M. Cabero, K. Crowston, A. K. Katsaggelos, S. L. Larson, T. K. Lee, C. Lintott, T. B. Littenberg, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, and V. Kalogera, “Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science,” Classical and Quantum Gravity
2017
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2018
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M. Asch, T. Moore, R. Badia, M. Beck, P. Beckman, T. Bidot, F. Bodin, F. Cappello, A. Choudhary, B. de Supinski, E. Deelman, J. Dongarra, A. Dubey, G. Fox, H. Fu, S. Girona, W. Gropp, M. Heroux, Y. Ishikawa, K. Keahey, D. Keyes, W. Kramer, J.-F. Lavignon, Y. Lu, S. Matsuoka, B. Mohr, D. Reed, S. Requena, J. Saltz, T. Schulthess, R. Stevens, M. Swany, A. Szalay, W. Tang, G. Varoquaux, J.-P. Vilotte, R. Wisniewski, Z. Xu, and I. Zacharov, “Big data and extreme-scale computing: Pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry,” The International Journal of High Performance Computing Applications
2018
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D. George and E. A. Huerta, “Deep neural networks to enable real-time multimessenger astrophysics,” Phys. Rev. D
2018
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D. George and E. A. Huerta, “Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data,” Physics Letters B
2018
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H. Gabbard, M. Williams, F. Hayes, and C. Messenger, “Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy,” Physical Review Letters
2018
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M. C. Miller and N. Yunes, “The new frontier of gravitational waves,” Nature
2019
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E. A. Huerta, G. Allen, I. Andreoni, J. M. Antelis, E. Bachelet, G. B. Berriman, F. B. Bianco, R. Biswas, M. Carrasco Kind, K. Chard, M. Cho, P. S. Cowperthwaite, Z. B. Etienne, M. Fishbach, F. Forster, D. George, T. Gibbs, M. Graham, W. Gropp, R. Gruendl, A. Gupta, R. Haas, S. Habib, E. Jennings, M. W. G. Johnson, E. Katsavounidis, D. S. Katz, A. Khan, V. Kindratenko, W. T. C. Kramer, X. Liu, A. Mahabal, Z. Marka, K. McHenry, J. M. Miller, C. Moreno, M. S. Neubauer, S. Oberlin, A. R. Olivas, D. Petravick, A. Rebei, S. Rosofsky, M. Ruiz, A. Saxton, B. F. Schutz, A. Schwing, E. Seidel, S. L. Shapiro, H. Shen, Y. Shen, L. P. Singer, B. M. Sipocz, L. Sun, J. Towns, A. Tsokaros, W. Wei, J. Wells, T. J. Williams, J. Xiong, and Z. Zhao, “Enabling real-time multi-messenger astrophysics discoveries with deep learning,” Nature Reviews Physics
2019
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X. Fan, J. Li, X. Li, Y. Zhong, and J. Cao, “Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors,” Sci. China Phys. Mech. Astron
2019
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A. Rebei, E. A. Huerta, S. Wang, S. Habib, R. Haas, D. Johnson, and D. George, “Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers,” Phys. Rev. D
2019
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C. Dreissigacker, R. Sharma, C. Messenger, R. Zhao, and R. Prix, “Deep-Learning Continuous Gravitational Waves,” Phys. Rev. D
2019
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H. Shen, D. George, E. A. Huerta, and Z. Zhao, “Denoising gravitational waves with enhanced deep recurrent denoising auto-encoders,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
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2019
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A. J. K. Chua, C. R. Galley, and M. Vallisneri, “Reduced-order modeling with artificial neurons for gravitational-wave inference,” Phys. Rev. Lett
2019
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R. Chard, Z. Li, K. Chard, L. Ward, Y. Babuji, A. Woodard, S. Tuecke, B. Blaiszik, M. J. Franklin, and I. Foster, “Dlhub: Model and data serving for science,” in 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
2019
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H. Tan, J. Noronha-Hostler, and N. Yunes, “Neutron Star Equation of State in light of GW190814,” Phys. Rev. Lett
2020
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E. A. Huerta, A. Khan, E. Davis, C. Bushell, W. D. Gropp, D. S. Katz, V. Kindratenko, S. Koric, W. T. C. Kramer, B. McGinty, K. McHenry, and A. Saxton, “Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure,” Journal of Big Data
2020
Cited alongside, same era.
Singapore: Springer Singapore, 2020
E. A. Huerta and Z. Zhao, Advances in Machine and Deep Learning for Modeling and Real-Time Detection of Multi-messenger Sources · 2020
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V. Kalogera et al
2021
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D. McClelland et al
2021
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E. Cuoco, J. Powell, M. Cavaglià, K. Ackley, M. Bejger, C. Chatterjee, M. Coughlin, S. Coughlin, P. Easter, R. Essick, H. Gabbard, T. Gebhard, S. Ghosh, L. Haegel, A. Iess, D. Keitel, Z. Marka, S. Marka, F. Morawski, T. Nguyen, R. Ormiston, M. Puerrer, M. Razzano, K. Staats, G. Vajente, and D. Williams, “Enhancing Gravitational-Wave Science with Machine Learning,” Mach. Learn. Sci. Tech
2021
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Y.-C. Lin and J.-H. P. Wu, “Detection of gravitational waves using bayesian neural networks,” Phys. Rev. D
2021
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2021
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2020
Cited alongside, same era.
H. Wang, S. Wu, Z. Cao, X. Liu, and J.-Y. Zhu, “Gravitational-wave signal recognition of LIGO data by deep learning,” Phys. Rev. D
2020
Cited alongside, same era.
X.-R. Li, G. Babu, W.-L. Yu, and X.-L. Fan, “Some optimizations on detecting gravitational wave using convolutional neural network,” Front. Phys. (Beijing)
2020
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2020
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P. G. Krastev, “Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks,” Phys. Lett. B
2020
Cited alongside, same era.
M. B. Schäfer, F. Ohme, and A. H. Nitz, “Detection of gravitational-wave signals from binary neutron star mergers using machine learning,” Phys. Rev. D
2020
Cited alongside, same era.
C. Dreissigacker and R. Prix, “Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise,” Phys. Rev. D
2020
Cited alongside, same era.
B. Beheshtipour and M. A. Papa, “Deep learning for clustering of continuous gravitational wave candidates,” Phys. Rev. D
2020
Cited alongside, same era.
Later among the works it cites.
2021
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2021
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2021
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2021
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2021
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2021
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S. Khan and R. Green, “Gravitational-wave surrogate models powered by artificial neural networks,” Phys. Rev. D
2021
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J. Lee, S. H. Oh, K. Kim, G. Cho, J. J. Oh, E. J. Son, and H. M. Lee, “Deep learning model on gravitational waveforms in merging and ringdown phases of binary black hole coalescences,” Phys. Rev. D
2021
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W. Wei, E. A. Huerta, M. Yun, N. Loutrel, M. A. Shaikh, P. Kumar, R. Haas, and V. Kindratenko, “Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence,” Astrophys. J
2021
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W. Wei and E. A. Huerta, “Deep learning for gravitational wave forecasting of neutron star mergers,” Phys. Lett. B
2021
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H. Yu, R. X. Adhikari, R. Magee, S. Sachdev, and Y. Chen, “Early warning of coalescing neutron-star and neutron-star-black-hole binaries from the nonstationary noise background using neural networks,” Phys. Rev. D
2021
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W. Wei, A. Khan, E. A. Huerta, X. Huang, and M. Tian, “Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers,” Physics Letters B
2021
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E. A. Huerta, A. Khan, X. Huang, M. Tian, M. Levental, R. Chard, W. Wei, M. Heflin, D. S. Katz, V. Kindratenko, D. Mu, B. Blaiszik, and I. Foster, “Accelerated, scalable and reproducible AI-driven gravitational wave detection,” Nature Astronomy
2021
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https://github.com/NVIDIA/TensorRT
NVIDIA, “TensorRT,” 2021 · 2021
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A. H. Nitz, I. W. Harry, D. A. Brown, C. M. Biwer, J. L. Willis, T. Dal Canton, C. D. Capano, L. P. Pekowsky, T. Dent, A. R. Williamson, G. S. Davies, S. De, , M. Cabero, B. Machenschalk, P. Kumar, S. Reyes, D. MacLeod, D. Finstad, F. Pannarale, T. Massinger, S. Kumar, M. Tapai, L. Singer, S. Khan, S. Fairhurst, A. Nielsen, and S. Singh, “PyCBC. Free and open software to study gravitational waves,” 2021
2021
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https://github.com/onnx/tensorflow-onnx
TensorFlow-ONNX, “Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX,” 2021 · 2021
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https://onnx.ai
ONNX Community, “Open Neural Network Exchange,” 2021 · 2021
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https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html
Anaconda, “Conda,” 2021 · 2021
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H. Shen, E. A. Huerta, E. O’Shea, P. Kumar, and Z. Zhao, “Statistically-informed deep learning for gravitational wave parameter estimation,” Mach. Learn. Sci. Tech
2022
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A. Khan, E. A. Huerta, and H. Zheng, “Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers,” Phys. Rev. D
2022
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