Gravitational-wave surrogate models powered by artificial neural networks
暂无分享,去创建一个
[1] Scott E. Field,et al. Surrogate model of hybridized numerical relativity binary black hole waveforms , 2018, Physical Review D.
[2] Flanagan,et al. Gravitational waves from merging compact binaries: How accurately can one extract the binary's parameters from the inspiral waveform? , 1994, Physical review. D, Particles and fields.
[3] Daniel Williams,et al. Precessing numerical relativity waveform surrogate model for binary black holes: A Gaussian process regression approach , 2019 .
[4] Michael Purrer,et al. Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal , 2015, 1508.07250.
[5] Chad R. Galley,et al. Reduced-Order Modeling with Artificial Neurons for Gravitational-Wave Inference. , 2019, Physical review letters.
[6] Brookhaven National Laboratory,et al. Accelerating parameter inference with graphics processing units , 2019, Physical Review D.
[7] G. Pratten,et al. Multipolar effective one body model for nonspinning black hole binaries , 2019, Physical Review D.
[8] Jian-Xun Wang,et al. Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning , 2019, Physica D: Nonlinear Phenomena.
[9] Omer San,et al. An artificial neural network framework for reduced order modeling of transient flows , 2018, Commun. Nonlinear Sci. Numer. Simul..
[10] I. Mandel,et al. Rapidly evaluating the compact-binary likelihood function via interpolation , 2013, 1305.3798.
[11] Michael Purrer,et al. Fast and accurate inference on gravitational waves from precessing compact binaries , 2016, 1604.08253.
[12] I. Mandel,et al. Towards Rapid Parameter Estimation on Gravitational Waves from Compact Binaries using Interpolated Waveforms , 2012, 1211.1254.
[13] Jan S. Hesthaven,et al. Fast prediction and evaluation of gravitational waveforms using surrogate models , 2013, ArXiv.
[14] Drew Keppel,et al. Interpolating compact binary waveforms using the singular value decomposition , 2011, 1108.5618.
[15] Parameswaran Ajith,et al. Accurate inspiral-merger-ringdown gravitational waveforms for nonspinning black-hole binaries including the effect of subdominant modes , 2017, 1708.03501.
[16] Scott E. Field,et al. A Surrogate model of gravitational waveforms from numerical relativity simulations of precessing binary black hole mergers , 2017, 1701.00550.
[17] W. Marsden. I and J , 2012 .
[18] L. Haegel,et al. Predicting the properties of black-hole merger remnants with deep neural networks , 2019, Classical and Quantum Gravity.
[19] M. Colleoni,et al. Multimode frequency-domain model for the gravitational wave signal from nonprecessing black-hole binaries , 2020, 2001.10914.
[20] Michael Boyle,et al. Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors , 2016, 1611.03703.
[21] R. Poggiani. Multi-messenger Observations of a Binary Neutron Star Merger , 2019, Proceedings of Frontier Research in Astrophysics – III — PoS(FRAPWS2018).
[22] D. Barta,et al. Fast prediction and evaluation of eccentric inspirals using reduced-order models , 2018, Physical Review D.
[23] ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019 .
[24] R. Cotesta,et al. Frequency-domain reduced-order model of aligned-spin effective-one-body waveforms with higher-order modes , 2020, 2003.12079.
[25] Darina Dvinskikh,et al. On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).
[26] N. Nguyen,et al. A general multipurpose interpolation procedure: the magic points , 2008 .
[27] Lawrence E. Kidder,et al. Aligned-spin neutron-star–black-hole waveform model based on the effective-one-body approach and numerical-relativity simulations , 2020, Physical Review D.
[28] Tsuyoshi Murata,et al. {m , 1934, ACML.
[29] Frank Ohme,et al. Phenomenological model for the gravitational-wave signal from precessing binary black holes with two-spin effects , 2018, Physical Review D.
[30] E. Porter,et al. Bayesian inference for binary neutron star inspirals using a Hamiltonian Monte Carlo algorithm , 2018, Physical Review D.
[31] Yoshinta Setyawati,et al. Regression methods in waveform modeling: a comparative study , 2019, Classical and Quantum Gravity.
[32] C. Pankow,et al. Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences , 2015, 1502.04370.
[33] S. McWilliams,et al. Optimizing spinning time-domain gravitational waveforms for advanced LIGO data analysis , 2016, 1601.03393.
[34] Walter Hoffmann,et al. Iterative algorithms for Gram-Schmidt orthogonalization , 1989, Computing.
[35] C. Mishra,et al. Including mode mixing in a higher-multipole model for gravitational waveforms from nonspinning black-hole binaries , 2019, Physical Review D.
[36] Y. Bouffanais,et al. Gravitational-wave detection rates for compact binaries formed in isolation: LIGO/Virgo O3 and beyond , 2019, Physical Review D.
[37] C. García-Quirós,et al. Accelerating the evaluation of inspiral–merger–ringdown waveforms with adapted grids , 2020, Classical and Quantum Gravity.
[38] Michael Purrer,et al. Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era , 2015, 1508.07253.
[39] Michele Vallisneri,et al. Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference. , 2020, Physical review letters.
[40] Frank Ohme,et al. Including higher order multipoles in gravitational-wave models for precessing binary black holes , 2020 .
[41] Thibault Damour,et al. Time-domain effective-one-body gravitational waveforms for coalescing compact binaries with nonprecessing spins, tides, and self-spin effects , 2018, Physical Review D.
[42] C. Messenger,et al. Deep-learning continuous gravitational waves , 2019, Physical Review D.
[43] S. Roy,et al. Effectual template banks for upcoming compact binary searches in Advanced-LIGO and Virgo data , 2017, Physical Review D.
[44] Andrea Taracchini,et al. Enriching the symphony of gravitational waves from binary black holes by tuning higher harmonics , 2018, Physical Review D.
[45] Shirley Ho,et al. Gravitational-wave population inference with deep flow-based generative network , 2020, Physical Review D.
[46] S. Privitera,et al. Searching for Gravitational Waves from Compact Binaries with Precessing Spins , 2016, 1603.02444.
[47] A Hamiltonian Monte–Carlo method for Bayesian inference of supermassive black hole binaries , 2014, 1311.7539.
[48] Shang-hai chiao tʿung ta hsüeh. Communications on pure and applied analysis , 2002 .
[50] Zachariah B. Etienne,et al. Improving performance of SEOBNRv3 by ∼300× , 2018, Classical and Quantum Gravity.
[51] Cambridge,et al. Gravitational wave parameter estimation with compressed likelihood evaluations , 2013, 1304.0462.
[52] E. Clothiaux,et al. Neural Networks and Their Applications , 1994 .
[53] C. Broeck,et al. Matter imprints in waveform models for neutron star binaries: Tidal and self-spin effects , 2018, Physical Review D.
[54] Lawrence E. Kidder,et al. Multipolar effective-one-body waveforms for precessing binary black holes: Construction and validation , 2020, Physical Review D.
[55] B. A. Boom,et al. Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA , 2013, Living Reviews in Relativity.
[56] Vincent Delmas,et al. Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling , 2021, J. Comput. Phys..
[57] Drew Keppel,et al. Interpolation in waveform space: enhancing the accuracy of gravitational waveform families using numerical relativity , 2013 .
[59] N. Nguyen,et al. An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .
[60] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[61] F. S. Guzmán,et al. Time series analysis of gravitational wave signals using neural networks , 2015 .
[62] Rory Smith,et al. Accelerated gravitational wave parameter estimation with reduced order modeling. , 2014, Physical review letters.
[63] F. Ohme,et al. Enhancing gravitational waveform models through dynamic calibration , 2018, Physical Review D.
[64] R. Stephenson. A and V , 1962, The British journal of ophthalmology.
[65] Marlin B. Schäfer,et al. Detection of gravitational-wave signals from binary neutron star mergers using machine learning , 2020, Physical Review D.
[66] P. K. Panda,et al. GW190814: Gravitational Waves from the Coalescence of a 23 M$_\odot$ Black Hole with a 2.6 M$_\odot$ Compact Object , 2020, 2006.12611.
[67] Scott E. Field,et al. Surrogate models for precessing binary black hole simulations with unequal masses , 2019, Physical Review Research.
[68] A. Samajdar,et al. Improving the NRTidal model for binary neutron star systems , 2019, Physical Review D.
[70] Kurunathan Ratnavelu,et al. FRONTIERS IN PHYSICS , 2009 .
[71] Frank Ohme,et al. First Higher-Multipole Model of Gravitational Waves from Spinning and Coalescing Black-Hole Binaries. , 2017, Physical review letters.
[72] Maria Alessandra Papa,et al. Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates , 2020, ArXiv.
[73] Matthew West,et al. The PyCBC search for gravitational waves from compact binary coalescence , 2015, 1508.02357.
[74] D. Keitel,et al. Faster search for long gravitational-wave transients: GPU implementation of the transient F-statistic , 2018, Classical and Quantum Gravity.
[76] G. Pratten,et al. Gravitational-wave selection effects using neural-network classifiers , 2020, Physical Review D.
[77] S. Duane,et al. Hybrid Monte Carlo , 1987 .
[78] G. Pratten,et al. Multipolar effective one body waveform model for spin-aligned black hole binaries , 2020, 2001.09082.
[79] Michael Pürrer,et al. Frequency-domain reduced order models for gravitational waves from aligned-spin compact binaries , 2014 .
[80] Baijiong Lin,et al. Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks , 2019, Frontiers of Physics.
[81] E. Thrane,et al. Parallelized inference for gravitational-wave astronomy , 2019, Physical Review D.
[82] Jan S. Hesthaven,et al. Non-intrusive reduced order modeling of nonlinear problems using neural networks , 2018, J. Comput. Phys..
[83] Michael Boyle,et al. Numerical relativity waveform surrogate model for generically precessing binary black hole mergers , 2017, 1705.07089.
[84] I. Mandel,et al. Accelerating gravitational wave parameter estimation with multi-band template interpolation , 2017, 1703.02062.
[85] A. Nagar,et al. Efficient effective one body time-domain gravitational waveforms , 2018, Physical Review D.