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Smooth equations of state for high-accuracy simulations of neutron star binaries

Foucart, F. and Duez, M. and Gudinas, A. and Hébert, F. and Kidder, L. and Pfeiffer, H. and Scheel, M. (2019) Smooth equations of state for high-accuracy simulations of neutron star binaries. Physical Review D, 100 (10). Art. No. 104048. ISSN 2470-0010. doi:10.1103/physrevd.100.104048.

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High-accuracy numerical simulations of merging neutron stars play an important role in testing and calibrating the waveform models used by gravitational wave observatories. Obtaining high-accuracy waveforms at a reasonable computational cost, however, remains a significant challenge. One issue is that high-order convergence of the solution requires the use of smooth evolution variables, while many of the equations of state used to model the neutron star matter have discontinuities, typically in the first derivative of the pressure. Spectral formulations of the equation of state have been proposed as a potential solution to this problem. Here, we report on the numerical implementation of spectral equations of state in the spectral Einstein code. We show that, in our code, spectral equations of state allow for high-accuracy simulations at a lower computational cost than commonly used “piecewise polytrope” equations state. We also demonstrate that not all spectral equations of state are equally useful: different choices for the low-density part of the equation of state can significantly impact the cost and accuracy of simulations. As a result, simulations of neutron star mergers present us with a trade-off between the cost of simulations and the physical realism of the chosen equation of state.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Foucart, F.0000-0003-4617-4738
Duez, M.0000-0002-0050-1783
Kidder, L.0000-0001-5392-7342
Pfeiffer, H.0000-0001-9288-519X
Additional Information:© 2019 American Physical Society. Received 19 August 2019; published 25 November 2019. F. F. gratefully acknowledges support from the NSF through Grant No. PHY-1806278, and from NASA through Grant No. 80NSSC18K0565. M. D. gratefully acknowledges support from the NSF through Grant No. PHY-1806207. H. P. gratefully acknowledges support from the NSERC Canada. L. K. acknowledges support from NSF Grants No. PHY-1606654 and No. PHY-1912081. F. H. and M. S. acknowledge support from NSF Grants No. PHY-170212 and No. PHY-1708213. F. H., L. K. and M. S. also thank the Sherman Fairchild Foundation for their support. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center (Grant No. HEC-SMD-17-1217). This research is also part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (Awards No. OCI-0725070 and No. ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana, Champaign, and its National Center for Supercomputing Applications. Computations were performed on Trillian, a Cray XE6m-200 supercomputer at UNH supported by the NSF MRI program under Grant No. PHY-1229408.
Group:TAPIR, Walter Burke Institute for Theoretical Physics
Funding AgencyGrant Number
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Sherman Fairchild FoundationUNSPECIFIED
State of IllinoisUNSPECIFIED
Issue or Number:10
Record Number:CaltechAUTHORS:20191125-142441489
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100042
Deposited By: Tony Diaz
Deposited On:25 Nov 2019 23:18
Last Modified:16 Nov 2021 17:51

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