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In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?".
Smith J, Gossett P (1984) A flexible sampling-rate conversion method. In: ICASSP ’84. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 9, pp 112–115
1984
Earlier work this paper cites.
MacKay DJC (2003) Information Theory, Inference, and Learning Algorithms. Cambridge University Press, USA
2003
Earlier work this paper cites.
Dimmelmeier H, Ott CD, Marek A, Janka HT (2008) The gravitational wave burst signal from core collapse of rotating stars. Physical Review D 78:064,056
2008
Earlier work this paper cites.
Röver C, Bizouard MA, Christensen N, Dimmelmeier H, Heng I, Meyer R (2009) Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce. Physical Review D - Particles, Fields, Gravitation and Cosmology 80(10), doi: 10.1103/PhysRevD.80.102004
2009
Earlier work this paper cites.
Janka HT (2012) Explosion mechanisms of core-collapse supernovae. Annual Review of Nuclear and Particle Science 62(1):407–451, doi: 10.1146/annurev-nucl-102711-094901 , URL https://doi.org/10.1146/annurev-nucl-102711-094901 , eprint https://doi.org/10.1146/annurev-nucl-102711-094901
2012
Earlier work this paper cites.
Lattimer JM (2012) The nuclear equation of state and neutron star masses. Annual Review of Nuclear and Particle Science 62(1):485–515, doi: 10.1146/annurev-nucl-102711-095018 , URL https://doi.org/10.1146/annurev-nucl-102711-095018 , eprint https://doi.org/10.1146/annurev-nucl-102711-095018
2012
Earlier work this paper cites.
Logue J, Ott CD, Heng I, Kalmus P, Scargill JHC (2012) Inferring core-collapse supernova physics with gravitational waves. Phys Rev D 86:044,023, doi: 10.1103/PhysRevD.86.044023 , URL https://link.aps.org/doi/10.1103/PhysRevD.86.044023
2012
Earlier work this paper cites.
Somiya K (2012) Detector configuration of KAGRA–the japanese cryogenic gravitational-wave detector. Classical and Quantum Gravity 29(12):124,007, doi: 10.1088/0264-9381/29/12/124007 , URL https://doi.org/10.1088%2F0264-9381%2F29%2F12%2F124007
2012
Earlier work this paper cites.
Abdikamalov E, Gossan S, DeMaio AM, Ott CD (2014) Measuring the angular momentum distribution in core-collapse supernova progenitors with gravitational waves. Physical Review D 90:044,001
2014
Earlier work this paper cites.
Acernese F, et al (2014) Advanced Virgo: a second-generation interferometric gravitational wave detector. Classical and Quantum Gravity 32(2):024,001, doi: 10.1088/0264-9381/32/2/024001 , URL https://doi.org/10.1088%2F0264-9381%2F32%2F2%2F024001
2014
Earlier work this paper cites.
Edwards MC, Meyer R, Christensen N (2014) Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations. Inverse Problems 30(11), doi: 10.1088/0266-5611/30/11/114008
2014
Earlier work this paper cites.
Aasi J, et al (2015) Advanced LIGO. Classical and Quantum Gravity 32(7):074,001, doi: 10.1088/0264-9381/32/7/074001 , URL https://doi.org/10.1088%2F0264-9381%2F32%2F7%2F074001
2015
Earlier work this paper cites.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115(3):211–252, doi: 10.1007/s11263-015-0816-y
2015
Cited alongside, same era.
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. The MIT Press
2016
Cited alongside, same era.
Gossan SE, Sutton P, Stuver A, Zanolin M, Gill K, Ott CD (2016) Observing gravitational waves from core-collapse supernovae in the advanced detector era. Phys Rev D 93:042,002, doi: 10.1103/PhysRevD.93.042002 , URL https://link.aps.org/doi/10.1103/PhysRevD.93.042002
2016
Cited alongside, same era.
Powell J, Gossan SE, Logue J, Heng IS (2016) Inferring the core-collapse supernova explosion mechanism with gravitational waves. Phys Rev D 94:123,012, doi: 10.1103/PhysRevD.94.123012 , URL https://link.aps.org/doi/10.1103/PhysRevD.94.123012
2016
Cited alongside, same era.
Gabbard H, Williams M, Hayes F, Messenger C (2018) Matching matched filtering with deep networks for gravitational-wave astronomy. Phys Rev Lett 120:141,103, doi: 10.1103/PhysRevLett.120.141103 , URL https://link.aps.org/doi/10.1103/PhysRevLett.120.141103
2018
Later among the works it cites.
George D, Shen H, Huerta EA (2018) Classification and unsupervised clustering of LIGO data with deep transfer learning. Phys Rev D 97:101,501, doi: 10.1103/PhysRevD.97.101501 , URL https://link.aps.org/doi/10.1103/PhysRevD.97.101501
2018
Later among the works it cites.
Chan ML, Heng I, Messenger C (2019) Detection and classification of supernova gravitational waves signals: A deep learning approach. arXivorg URL http://search.proquest.com/docview/2331700621/
2019
Later among the works it cites.
Dreissigacker C, Sharma R, Messenger C, Zhao R, Prix R (2019) Deep-learning continuous gravitational waves. Phys Rev D 100:044,009, doi: 10.1103/PhysRevD.100.044009 , URL https://link.aps.org/doi/10.1103/PhysRevD.100.044009
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Richers S, Ott CD, Abdikamalov E, O’Connor E, Sullivan C (2016) Equation of State Effects on Gravitational Waves from Rotating Core Collapse. doi: 10.5281/zenodo.201145 , URL https://doi.org/10.5281/zenodo.201145
2016
Cited alongside, same era.
George D, Huerta E (2018) Deep learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data. Physics Letters B 778:64 – 70, doi: https://doi.org/10.1016/j.physletb.2017.12.053 , URL http://www.sciencedirect.com/science/article/pii/S0370269317310390
2017
Cited alongside, same era.
Kuroda T, Kotake K, Hayama K, Takiwaki T (2017) Correlated signatures of gravitational-wave and neutrino emission in three-dimensional general-relativistic core-collapse supernova simulations. The Astrophysical Journal 851(1):62, doi: 10.3847/1538-4357/aa988d , URL https://doi.org/10.3847%2F1538-4357%2Faa988d
2017
Cited alongside, same era.
Powell J, Szczepanczyk M, Heng IS (2017) Inferring the core-collapse supernova explosion mechanism with three-dimensional gravitational-wave simulations. Phys Rev D 96:123,013, doi: 10.1103/PhysRevD.96.123013 , URL https://link.aps.org/doi/10.1103/PhysRevD.96.123013
2017
Cited alongside, same era.
Richers S, Ott CD, Abdikamalov E, O’Connor E, Sullivan C (2017) Equation of state effects on gravitational waves from rotating core collapse. Phys Rev D 95:063,019, doi: 10.1103/PhysRevD.95.063019 , URL https://link.aps.org/doi/10.1103/PhysRevD.95.063019
2017
Cited alongside, same era.
Zevin M, Coughlin S, Bahaadini S, Besler E, Rohani N, Allen S, Cabero M, Crowston K, Katsaggelos AK, Larson SL, Lee TK, Lintott C, Littenberg TB, Lundgren A, Østerlund C, Smith JR, Trouille L, Kalogera V (2017) Gravity Spy: integrating Advanced LIGO detector characterization, machine learning, and citizen science. Classical and Quantum Gravity 34(6):064,003, doi: 10.1088/1361-6382/aa5cea , URL https://doi.org/10.1088%2F1361-6382%2Faa5cea
2017
Cited alongside, same era.
Astone P, Cerdá-Durán P, Di Palma I, Drago M, Muciaccia F, Palomba C, Ricci F (2018) New method to observe gravitational waves emitted by core collapse supernovae. Phys Rev D 98:122,002, doi: 10.1103/PhysRevD.98.122002 , URL https://link.aps.org/doi/10.1103/PhysRevD.98.122002
2018
Cited alongside, same era.
Chollet F (2018) Deep Learning with Python. Manning Publications Co., Shelter Island, New York
2018
Cited alongside, same era.
2019
Later among the works it cites.
Gabbard H, Messenger C, Heng IS, Tonolini F, Murray-Smith R (2019) Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. arXiv:190906296 [astro-phIM]
2019
Later among the works it cites.
Shen H, Huerta EA, Zhao Z, Jennings E, Sharma H (2019) Deterministic and Bayesian neural networks for low-latency gravitational wave parameter estimation of binary black hole mergers. arXiv:190301998 [gr-qc]
2019
Later among the works it cites.
Abbott B, et al (2020) Optically targeted search for gravitational waves emitted by core-collapse supernovae during the first and second observing runs of Advanced LIGO and Advanced Virgo. Phys Rev D 101:084,002, doi: 10.1103/PhysRevD.101.084002 , URL https://link.aps.org/doi/10.1103/PhysRevD.101.084002
2020
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Chua AJK, Vallisneri M (2020) Learning Bayesian posteriors with neural networks for gravitational-wave inference. Phys Rev Lett 124:041,102, doi: 10.1103/PhysRevLett.124.041102 , URL https://link.aps.org/doi/10.1103/PhysRevLett.124.041102
2020
Closest in time.
Green SR, Gair J (2020) Complete parameter inference for gw150914 using deep learning. arXiv:200803312 [astro-phIM]
2020
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Green SR, Simpson C, Gair J (2020) Gravitational-wave parameter estimation with autoregressive neural network flows. arXiv:200207656 [astro-phIM]
2020
Closest in time.
Iess A, Cuoco E, Morawski F, Powell J (2020) Core-collapse supernova gravitational-wave search and deep learning classification. Machine Learning: Science and Technology 1(2):025,014, doi: 10.1088/2632-2153/ab7d31 , URL https://doi.org/10.1088%2F2632-2153%2Fab7d31
2020
Closest in time.