Forward-Backward Cylindrical Beamformer and Geometric Spreading Weighting for Seismic Surface Wave Parameter Estimation

2007 ◽  
Author(s):  
Daren J. Zywicki ◽  
Sastry Malladi
Keyword(s):  
Parameter Estimation ◽  
Surface Wave ◽  
Wave Parameter ◽  
Geometric Spreading

scholarly journals Gravitational wave parameter estimation with compressed likelihood evaluations

2013 ◽  
Vol 87 (12) ◽  
Author(s):  
Priscilla Canizares ◽  
Scott E. Field ◽  
Jonathan R. Gair ◽  
Manuel Tiglio
Keyword(s):  
Parameter Estimation ◽  
Gravitational Wave ◽  
Wave Parameter

Wave parameter estimation using neural networks

2004 ◽  
Vol 17 (7) ◽  
pp. 536-550 ◽  
Author(s):  
J.D. Agrawal ◽  
M.C. Deo
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Wave Parameter

scholarly journals Statistically-informed deep learning for gravitational wave parameter estimation

2021 ◽  
Author(s):  
Hongyu Shen ◽  
Eliu Huerta ◽  
Eamonn O’Shea ◽  
Prayush Kumar ◽  
Zhizhen Zhao
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Network Models ◽  
Wave Parameter ◽  
Posterior Distributions ◽  
Neural Network Models ◽  
The Masses ◽  
Bayesian Methodologies

Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

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