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Published August 1, 2019 | Published + Accepted Version
Journal Article Open

The NANOGrav 11 yr Data Set: Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries


Observations indicate that nearly all galaxies contain supermassive black holes at their centers. When galaxies merge, their component black holes form SMBH binaries (SMBHBs), which emit low-frequency gravitational waves (GWs) that can be detected by pulsar timing arrays. We have searched the North American Nanohertz Observatory for Gravitational Waves 11 yr data set for GWs from individual SMBHBs in circular orbits. As we did not find strong evidence for GWs in our data, we placed 95% upper limits on the strength of GWs from such sources. At f_(gw) = 8 nHz, we placed a sky-averaged upper limit of h_0 < 7.3(3) × 10^(−15). We also developed a technique to determine the significance of a particular signal in each pulsar using "dropout" parameters as a way of identifying spurious signals. From these upper limits, we ruled out SMBHBs emitting GWs with f_(gw) = 8 nHz within 120 Mpc for M = 10^9 M⊙, and within 5.5 Gpc for M = 10^(10)M⊙ at our most sensitive sky location. We also determined that there are no SMBHBs with M > 1.6 x 10^9 M⊙ emitting GWs with f_(gw) = 2.8–317.8 nHz in the Virgo Cluster. Finally, we compared our strain upper limits to simulated populations of SMBHBs, based on galaxies in the Two Micron All-Sky Survey and merger rates from the Illustris cosmological simulation project, and found that only 34 out of 75,000 realizations of the local universe contained a detectable source.

Additional Information

© 2019 The American Astronomical Society. Received 2018 December 28; revised 2019 April 17; accepted 2019 May 15; published 2019 July 31. This paper is the result of the work of dozens of people over the course of more than 13 years. We list specific contributions below. Z.A., K.C., P.B.D., M.E.D., T.D., J.A.E., E.C.F., R.D.F., E.F., P.A.G., G.J., M.L.J., M.T.L., L.L., D.R.L., R.S.L., M.A.M., C.N., D.J.N., T.T.P., S.M.R., P.S.R., R.S., I.H.S., K.S., J.K.S., and W.Z. developed the 11 yr data set. S.J.V. led the search and coordinated the paper writing. J.A.E., S.R.T., P.T.B., S.J.V., and C.A.W. designed and implemented the Bayesian search algorithms in enterprise, and A.R.K., S.T.M., S.R.T., and S.J.V. implemented the Fp-statistic in enterprise. N.J.C. helped to uncover a bug in an early implementation of the search algorithms. P.T.B. developed the jump proposals based on empirical distributions. S.J.V., K.I., J.E.T., K.A., P.R.B., A.M.H., and N.S.P. ran the searches. M.R.B, J.A.E., and S.R.T. performed an initial analysis of the 9 yr data set. C.M.F.M., K.I., T.J.W.L., J.S., and S.J.V. did the astrophysical interpretation. S.J.V., K.I., and C.M.F.M. wrote the paper and generated the plots. We thank the anonymous referee for helpful comments and suggestions that improved this manuscript. The NANOGrav project receives support from National Science Foundation (NSF) Physics Frontier Center award number 1430284. NANOGrav research at UBC is supported by an NSERC Discovery Grant and Discovery Accelerator Supplement and by the Canadian Institute for Advanced Research. Portions of this research were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. M.V. and J.S. acknowledge support from the JPL RTD program. S.R.T. was partially supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, administered by Oak Ridge Associated Universities through a contract with NASA. M.A.M., S.B.S., D.R.L., S.T.M., and P.R.B. were supported by NSF award number #1458952. M.A.M. and N.P. were supported by NSF award number #1517003. J.A.E. was partially supported by NASA through Einstein Fellowship grants PF4-150120. S.B.S. and C.A.W. were supported by NSF award #1815664. W.W.Z. is supported by the Chinese Academy of Science Pioneer Hundred Talents Program, the Strategic Priority Research Program of the Chinese Academy of Sciences grant No. XDB23000000, the National Natural Science Foundation of China grant No. 11690024, and by the Astronomical Big Data Joint Research Center, co-founded by the National Astronomical Observatories, Chinese Academy of Sciences and the Alibaba Cloud. Portions of this work performed at N.R.L. are supported by the Chief of Naval Research. The Flatiron Institute is supported by the Simons Foundation. We thank Logan O'Beirne for helping to uncover a bug in an early implementation of the search algorithms, and for providing comments on the algorithms and manuscript. We thank Casey McGrath for performing some of the CW analyses. We thank Luke Kelley and Tingting Liu for helpful discussions. We thank Alberto Sesana, Xinjiang Zhu, and Siyuan Chen for providing useful comments on the manuscript. We are grateful for computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin-Milwaukee, which is supported by NSF Grants 0923409 and 1626190. This research made use of the Super Computing System (Spruce Knob) at WVU, which is funded in part by the National Science Foundation EPSCoR Research Infrastructure Improvement Cooperative Agreement #1003907, the state of West Virginia (WVEPSCoR via the Higher Education Policy Commission) and WVU. This research also 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 (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. Data for this project were collected using the facilities of the Green Bank Observatory and the Arecibo Observatory. The Green Bank Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Arecibo Observatory is a facility of the National Science Foundation operated under cooperative agreement by the University of Central Florida in alliance with Yang Enterprises, Inc. and Universidad Metropolitana. Software: enterprise (Ellis et al. 2017), PAL2 (Ellis & van Haasteren 2017a), PTMCMCSampler (Ellis & van Haasteren 2017b).

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Accepted Version - 1812.11585.pdf

Published - Aggarwal_2019_ApJ_880_116.pdf


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August 19, 2023
August 19, 2023