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Initial data and eccentricity reduction toolkit for binary black hole numerical relativity waveforms

Habib, Sarah and Ramos-Buades, Antoni and Huerta, E. A. and Husa, Sascha and Haas, Roland and Etienne, Zachariah (2021) Initial data and eccentricity reduction toolkit for binary black hole numerical relativity waveforms. Classical and Quantum Gravity, 38 (12). Art. No. 125007. ISSN 0264-9381. doi:10.1088/1361-6382/abe691. https://resolver.caltech.edu/CaltechAUTHORS:20210518-070729131

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Abstract

The production of numerical relativity waveforms that describe quasi-circular binary black hole mergers requires high-quality initial data, and an algorithm to iteratively reduce residual eccentricity. To date, these tools remain closed source, or in commercial software that prevents their use in high performance computing platforms. To address these limitations, and to ensure that the broader numerical relativity community has access to these tools, herein we provide all the required elements to produce high-quality numerical relativity simulations in supercomputer platforms, namely: open source parameter files to numerically simulate spinning black hole binaries with asymmetric mass-ratios; open source Python tools to produce high-quality initial data for numerical relativity simulations of spinning black hole binaries on quasi-circular orbits; and open source Python tools for eccentricity reduction, both as stand-alone software and also deployed in the Einstein Toolkit's software infrastructure. This open source toolkit fills in a void in the literature at a time when numerical relativity has an ever increasing role in the study and interpretation of gravitational wave sources. As part of our community building efforts, and to streamline and accelerate the use of these resources, we provide tutorials that describe, step by step, how to obtain and use these open source numerical relativity tools.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1361-6382/abe691DOIArticle
https://arxiv.org/abs/2011.08878arXivDiscussion Paper
https://github.com/NCSAGravity/eccredRelated ItemOpen Source Software
ORCID:
AuthorORCID
Habib, Sarah0000-0002-4725-4978
Ramos-Buades, Antoni0000-0002-6874-7421
Huerta, E. A.0000-0002-9682-3604
Husa, Sascha0000-0002-0445-1971
Haas, Roland0000-0003-1424-6178
Etienne, Zachariah0000-0002-6838-9185
Additional Information:© 2021 IOP Publishing Ltd. Received 25 November 2020, revised 27 January 2021; Accepted for publication 15 February 2021; Published 13 May 2021. EAH gratefully acknowledges National Science Foundation (NSF) awards OAC-1931561 and OAC-1934757. RH gratefully acknowledges NSF awards OAC-1550514, OAC-2004879, and ACI-1238993. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993), the State of Illinois, and as of December, 2019, the National Geospatial-Intelligence Agency. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. We acknowledge support from the NCSA and the Students Pushing INnovation (SPIN) undergraduate internship Program at NCSA. We thank the NCSA Gravity Group for useful feedback. NSF-1659702 and XSEDE TG-PHY160053 grants are gratefully acknowledged. Data availability statement: The data that support the findings of this study are available upon reasonable request from the authors.
Group:TAPIR, Walter Burke Institute for Theoretical Physics
Funders:
Funding AgencyGrant Number
NSFOAC-1931561
NSFOAC-1934757
NSFOAC-1550514
NSFOAC-2004879
NSFACI-1238993
NSFOCI-0725070
NSFACI-1238993
State of IllinoisUNSPECIFIED
National Geospatial-Intelligence AgencyUNSPECIFIED
NSFOAC-1659702
NSFTG-PHY160053
Issue or Number:12
DOI:10.1088/1361-6382/abe691
Record Number:CaltechAUTHORS:20210518-070729131
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210518-070729131
Official Citation:Sarah Habib et al 2021 Class. Quantum Grav. 38 125007
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109158
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 May 2021 19:03
Last Modified:19 May 2021 19:03

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