scholarly journals Improving gravitational-wave parameter estimation using Gaussian process regression

2016 ◽  
Vol 93 (6) ◽  
Author(s):  
Christopher J. Moore ◽  
Christopher P. L. Berry ◽  
Alvin J. K. Chua ◽  
Jonathan R. Gair
Keyword(s):  
Parameter Estimation ◽  
Gravitational Wave ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Wave Parameter

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

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.

scholarly journals Accelerated Gravitational Wave Parameter Estimation with Reduced Order Modeling

2015 ◽  
Vol 114 (7) ◽  
Author(s):  
Priscilla Canizares ◽  
Scott E. Field ◽  
Jonathan Gair ◽  
Vivien Raymond ◽  
Rory Smith ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Gravitational Wave ◽  
Reduced Order Modeling ◽  
Wave Parameter ◽  
Reduced Order

scholarly journals Exploring the Parameter Space of Compact Binary Population Synthesis

2016 ◽  
Vol 12 (S325) ◽  
pp. 46-50 ◽  
Author(s):  
Jim W. Barrett ◽  
Ilya Mandel ◽  
Coenraad J. Neijssel ◽  
Simon Stevenson ◽  
Alejandro Vigna-Gómez
Keyword(s):  
Gravitational Wave ◽  
Gaussian Process ◽  
Parameter Space ◽  
Gaussian Process Regression ◽  
Compact Object ◽  
Population Synthesis ◽  
Preliminary Results ◽  
Compact Binary ◽  
Binary Evolution

AbstractAs we enter the era of gravitational wave astronomy, we are beginning to collect observations which will enable us to explore aspects of astrophysics of massive stellar binaries which were previously beyond reach. In this paper we describe COMPAS (Compact Object Mergers: Population Astrophysics and Statistics), a new platform to allow us to deepen our understanding of isolated binary evolution and the formation of gravitational-wave sources. We describe the computational challenges associated with their exploration, and present preliminary results on overcoming them using Gaussian process regression as a simulation emulation technique.

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