Fetching the paper…
Reading the bibliography…
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.
M. Cabero et al. , Blip glitches in Advanced LIGO data (2019), arXiv:1901.05093
1901
Earlier work this paper cites.
G. Carleo et al. , Machine learning and the physical sciences (2019), arXiv:1903.10563
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
C. Dreissigacker et al. , Deep-Learning Continuous Gravitational Waves (2019), arXiv:1904.13291
1904
Earlier work this paper cites.
K. S. Thorne, Gravitational radiation, in Three hundred years of gravitation , edited by S. W. Hawking and W. Israel (Cambridge University Press, Cambridge, 1987) pp. 330–458
1987
Earlier work this paper cites.
Y. LeCun et al. , Backpropagation applied to handwritten zip code recognition, Neural Computation 1
1989
Earlier work this paper cites.
C. Cutler and É. E. Flanagan, Gravitational waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral waveform?, Physical Review D 49
1994
Earlier work this paper cites.
Y. LeCun and Y. Bengio, Convolutional networks for images, speech, and time-series, in The Handbook of Brain Theory and Neural Networks (The MIT Press, 1995)
1995
Earlier work this paper cites.
Y. LeCun et al. , Gradient-based learning applied to document recognition, Proceedings of the IEEE 86
1998
Earlier work this paper cites.
B. F. Schutz, Gravitational wave astronomy, Classical and Quantum Gravity 16
1999
Earlier work this paper cites.
B. Allen, χ 2 \chi^{2} time-frequency discriminator for gravitational wave detection, Physical Review D 71
2005
Earlier work this paper cites.
M. Maggiore, Gravitational Waves: Volume 1: Theory and Experiments (Oxford University Press, New York, 2008)
2008
Earlier work this paper cites.
Y. Bengio et al. , Curriculum Learning, in International Conference on Machine Learning (ICML) (2009)
2009
Earlier work this paper cites.
J. D. E. Creighton and W. G. Anderson, Gravitational-Wave Physics and Astronomy: An Introduction to Theory, Experiment and Data Analysis (Wiley-VCH, Weinheim, 2011)
2011
Earlier work this paper cites.
B. Allen et al. , FINDCHIRP: An Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries, Physical Review D 85
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Neural Information Processing Systems (NeurIPS) (2012)
2012
Earlier work this paper cites.
S. Babak et al. , Searching for gravitational waves from binary coalescence, Physical Review D 87
2013
Earlier work this paper cites.
Y. Aso et al. , Interferometer design of the KAGRA gravitational wave detector, Physical Review D 88
2013
Cited alongside, same era.
R. J. E. Smith, I. Mandel, and A. Vecchio, Studies of waveform requirements for intermediate mass-ratio coalescence searches with advanced gravitational-wave detectors, Physical Review D 88
2013
Cited alongside, same era.
C. Szegedy et al. , Intriguing properties of neural networks (2013), arXiv:1312.6199
2013
Cited alongside, same era.
F. Acernese et al. , Advanced Virgo: a second-generation interferometric gravitational wave detector, Classical and Quantum Gravity 32
2014
Cited alongside, same era.
Y. Kim, Convolutional neural networks for sentence classification (2014), arXiv:1408.5882
2014
C. Messick et al. , Analysis framework for the prompt discovery of compact binary mergers in gravitational-wave data, Physical Review D 95
2017
Later among the works it cites.
A. H. Nitz et al. , Detecting Binary Compact-object Mergers with Gravitational Waves: Understanding and Improving the Sensitivity of the PyCBC Search, The Astrophysical Journal 849
2017
Later among the works it cites.
2017
Later among the works it cites.
M. Zevin et al. , Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science, Classical and Quantum Gravity 34
2017
Later among the works it cites.
S. Bahaadini et al. , Deep multi-view models for glitch classification, in International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2017)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
W. Zhu et al. , Searching for pulsars using image pattern recognition, The Astrophysical Journal 781
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization (2014), arXiv:1412.6980
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, in European Conference on Computer Vision (ECCV) (2014) pp. 818–833
2014
Cited alongside, same era.
J. Aasi et al. , Advanced LIGO, Classical and Quantum Gravity 32
2015
Cited alongside, same era.
J. Veitch et al. , Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library, Physical Review D 91
2015
Cited alongside, same era.
M. Vallisneri et al. , The LIGO Open Science Center, Journal of Physics: Conference Series 610
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2017
Later among the works it cites.
C. Biwer et al. , Validating gravitational-wave detections: The Advanced LIGO hardware injection system, Physical Review D 95
2017
Later among the works it cites.
A. Bohé et al. , Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors, Physical Review D 95
2017
Later among the works it cites.
A. Paszke et al. , Automatic differentiation in PyTorch, Accepted at the “Autodiff Workshop” at NeurIPS (2017)
2017
Later among the works it cites.
C. Olah, A. Mordvintsev, and L. Schubert, Feature visualization, Distill 2
2017
Later among the works it cites.
H. Gabbard et al. , Matching matched filtering with deep networks for gravitational-wave astronomy, Physical Review Letters 120
2018
Later among the works it cites.
S. Caudill, Techniques for gravitational-wave detection of compact binary coalescence, in 26th European Signal Processing Conference (EUSIPCO) (2018)
2018
Later among the works it cites.
A. H. Nitz, Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers, Classical and Quantum Gravity 35
2018
Later among the works it cites.
M. Razzano and E. Cuoco, Image-based deep learning for classification of noise transients in gravitational wave detectors, Classical and Quantum Gravity 35
2018
Later among the works it cites.
S. Bahaadini et al. , Machine learning for Gravity Spy: Glitch classification and dataset, Information Sciences 444
2018
Later among the works it cites.
A. H. Nitz et al. , Rapid detection of gravitational waves from compact binary mergers with PyCBC Live, Physical Review D 98
2018
Later among the works it cites.
S. J. Reddi, S. Kale, and S. Kumar, On the convergence of Adam and beyond, in International Conference on Learning Representations (ICLR) (2018)
2018
Later among the works it cites.
A. H. Nitz et al. , PyCBC Release v1.13.5 (2019)
2019
Closest in time.