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Parameter estimation method that directly compares gravitational wave observations to numerical relativity

Lange, J. and Hemberger, D. A. and Scheel, M. A. and Szilágyi, B. and Teukolsky, S. (2017) Parameter estimation method that directly compares gravitational wave observations to numerical relativity. Physical Review D, 96 (10). Art. No. 104041. ISSN 2470-0010. https://resolver.caltech.edu/CaltechAUTHORS:20171122-095738196

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Abstract

We present and assess a Bayesian method to interpret gravitational wave signals from binary black holes. Our method directly compares gravitational wave data to numerical relativity (NR) simulations. In this study, we present a detailed investigation of the systematic and statistical parameter estimation errors of this method. This procedure bypasses approximations used in semianalytical models for compact binary coalescence. In this work, we use the full posterior parameter distribution for only generic nonprecessing binaries, drawing inferences away from the set of NR simulations used, via interpolation of a single scalar quantity (the marginalized log likelihood, lnL) evaluated by comparing data to nonprecessing binary black hole simulations. We also compare the data to generic simulations, and discuss the effectiveness of this procedure for generic sources. We specifically assess the impact of higher order modes, repeating our interpretation with both l ≤ 2 as well as l ≤ 3 harmonic modes. Using the l ≤ 3 higher modes, we gain more information from the signal and can better constrain the parameters of the gravitational wave signal. We assess and quantify several sources of systematic error that our procedure could introduce, including simulation resolution and duration; most are negligible. We show through examples that our method can recover the parameters for equal mass, zero spin, GW150914-like, and unequal mass, precessing spin sources. Our study of this new parameter estimation method demonstrates that we can quantify and understand the systematic and statistical error. This method allows us to use higher order modes from numerical relativity simulations to better constrain the black hole binary parameters.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevD.96.104041DOIArticle
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.96.104041PublisherArticle
https://arxiv.org/abs/1705.09833arXivDiscussion Paper
ORCID:
AuthorORCID
Teukolsky, S.0000-0001-9765-4526
Additional Information:© 2017 American Physical Society. Received 26 May 2017; published 22 November 2017. The RIT authors gratefully acknowledge the NSF for financial support from Grants No. PHY-1505629, No. AST-1664362, No. PHY-1607520, No. ACI-1550436, No. AST-1516150, and No. ACI-1516125. Computational resources were provided by XSEDE allocation Grant No. TG-PHY060027N, and by NewHorizons and BlueSky Clusters at Rochester Institute of Technology, which were supported by NSF Grants No. PHY-0722703, No. DMS-0820923, No. AST-1028087, and No. PHY-1229173. This research was also part of the Blue Waters sustained-petascale computing NSF projects Grants No. ACI-0832606, No. ACI-1238993, No. OCI-1515969, and No. OCI-0725070. The SXS Collaboration authors gratefully acknowledge the NSF for financial support from Grants No. PHY-1307489, No. PHY-1606522, No. PHY-1606654, and No. AST- 1333129. They also gratefully acknowledge support for this research at CITA from NSERC of Canada, the Ontario Early Researcher Awards Program, the Canada Research Chairs Program, and the Canadian Institute for Advanced Research. Calculations were done on the ORCA computer cluster, supported by NSF Grant No. PHY-1429873, the Research Corporation for Science Advancement, CSU Fullerton, the GPC supercomputer at the SciNet HPC Consortium [49]; SciNet is funded by the Canada Foundation for Innovation (CFI) under the auspices of Compute Canada, the Government of Ontario, Ontario Research Fund (ORF)—Research Excellence, and the University of Toronto. Further calculations were performed on the Briarée cluster at Sherbrooke University, managed by Calcul Québec and Compute Canada and with operation funded by the CFI, Ministére de l’Économie, de l’Innovation et des Exportations du Quebec (MEIE), RMGA and the Fonds de recherche du Québec—Nature et Technologies (FRQ-NT). The GT authors gratefully acknowledge the NSF for financial support from Grants No. ACI-1550461 and No. PHY-1505824. Computational resources were provided by XSEDE and the Georgia Tech Cygnus Cluster. Finally, the authors are grateful for computational resources used for the parameter estimation runs provided by the Leonard E. Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin—Milwaukee, the Albert Einstein Institute at Hanover, Germany, and the California Institute of Technology at Pasadena, California.
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NSFPHY-1505629
NSFAST-1664362
NSFPHY-1607520
NSFACI-1550436
NSFAST-1516150
NSFACI-1516125
NSFTG-PHY060027N
NSFPHY-0722703
NSFDMS-0820923
NSFAST-1028087
NSFPHY-1229173
NSFACI-0832606
NSFACI-1238993
NSFOCI-1515969
NSFOCI-0725070
NSFPHY-1307489
NSFPHY-1606522
NSFPHY-1606654
NSFAST-1333129
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Ontario Early Researcher Awards ProgramUNSPECIFIED
Canada Research Chairs ProgramUNSPECIFIED
Canadian Institute for Advanced Research (CIFAR)UNSPECIFIED
NSFPHY-1429873
Research CorporationUNSPECIFIED
California State University, FullertonUNSPECIFIED
Canada Foundation for InnovationUNSPECIFIED
Compute CanadaUNSPECIFIED
Government of OntarioUNSPECIFIED
Ontario Research FundUNSPECIFIED
University of TorontoUNSPECIFIED
Ministére de l’Économie, de l’Innovation et des Exportations du Quebec (MEIE)UNSPECIFIED
RMGAUNSPECIFIED
Fonds de recherche du Québec-Nature et Technologies (FRQ-NT)UNSPECIFIED
NSFACI-1550461
NSFPHY-1505824
Issue or Number:10
Record Number:CaltechAUTHORS:20171122-095738196
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171122-095738196
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83431
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Nov 2017 18:10
Last Modified:22 Nov 2019 09:58

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