TY - JOUR

T1 - Statistically-informed deep learning for gravitational wave parameter estimation

AU - Shen, Hongyu

AU - Huerta, E. A.

AU - O'Shea, Eamonn

AU - Kumar, Prayush

AU - Zhao, Zhizhen

N1 - Funding Information:
Neural network models are available at the Data and Deep Learning Hub for Science [83, 84]. E A H, H S and Z Z gratefully acknowledge National Science Foundation (NSF) awards OAC-1931561 and OAC-1934757. E O S and P K gratefully acknowledge NSF grants PHY-1912081 and OAC-193128, and the Sherman Fairchild Foundation. P K also acknowledges the support of the Department of Atomic Energy, Government of India, under Project No. RTI4001. This work utilized the Hardware-Accelerated Learning (HAL) cluster, supported by NSF Major Research Instrumentation program, Grant OAC-1725729, as well as the University of Illinois at Urbana-Champaign. Compute resources were provided by XSEDE using allocation TG-PHY160053. This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications and which is supported by funds from the University of Illinois at Urbana-Champaign. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. This research also made use of LIGO Data Grid clusters at the California Institute of Technology. This research used data, software and/or web tools obtained from the LIGO Open Science Center (https://gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes.
Publisher Copyright:
© 2021 The authors. Published by IOP Publishing Ltd.

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

KW - Deep learning

KW - Gravitational wave

KW - Parameter estimation

KW - Posterior inference

UR - http://www.scopus.com/inward/record.url?scp=85121638623&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85121638623&partnerID=8YFLogxK

U2 - 10.1088/2632-2153/ac3843

DO - 10.1088/2632-2153/ac3843

M3 - Article

AN - SCOPUS:85121638623

SN - 2632-2153

VL - 3

JO - Machine Learning: Science and Technology

JF - Machine Learning: Science and Technology

IS - 1

M1 - 015007

ER -