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How to Detect an Astrophysical Nanohertz Gravitational-Wave Background

Authors :
Bécsy, Bence
Cornish, Neil J.
Meyers, Patrick M.
Kelley, Luke Zoltan
Agazie, Gabriella
Anumarlapudi, Akash
Archibald, Anne M.
Arzoumanian, Zaven
Baker, Paul T.
Blecha, Laura
Brazier, Adam
Brook, Paul R.
Burke-Spolaor, Sarah
Casey-Clyde, J. Andrew
Charisi, Maria
Chatterjee, Shami
Chatziioannou, Katerina
Cohen, Tyler
Cordes, James M.
Crawford, Fronefield
Cromartie, H. Thankful
Crowter, Kathryn
DeCesar, Megan E.
Demorest, Paul B.
Dolch, Timothy
Ferrara, Elizabeth C.
Fiore, William
Fonseca, Emmanuel
Freedman, Gabriel E.
Garver-Daniels, Nate
Gentile, Peter A.
Glaser, Joseph
Good, Deborah C.
Gültekin, Kayhan
Hazboun, Jeffrey S.
Hourihane, Sophie
Jennings, Ross J.
Johnson, Aaron D.
Jones, Megan L.
Kaiser, Andrew R.
Kaplan, David L.
Kerr, Matthew
Key, Joey S.
Laal, Nima
Lam, Michael T.
Lamb, William G.
Lazio, T. Joseph W.
Lewandowska, Natalia
Littenberg, Tyson B.
Liu, Tingting
Lorimer, Duncan R.
Luo, Jing
Lynch, Ryan S.
Ma, Chung-Pei
Madison, Dustin R.
McEwen, Alexander
McKee, James W.
McLaughlin, Maura A.
McMann, Natasha
Meyers, Bradley W.
Mingarelli, Chiara M. F.
Mitridate, Andrea
Ng, Cherry
Nice, David J.
Ocker, Stella Koch
Olum, Ken D.
Pennucci, Timothy T.
Perera, Benetge B. P.
Pol, Nihan S.
Radovan, Henri A.
Ransom, Scott M.
Ray, Paul S.
Romano, Joseph D.
Sardesai, Shashwat C.
Schmiedekamp, Ann
Schmiedekamp, Carl
Schmitz, Kai
Shapiro-Albert, Brent J.
Siemens, Xavier
Simon, Joseph
Siwek, Magdalena S.
Fiscella, Sophia V. Sosa
Stairs, Ingrid H.
Stinebring, Daniel R.
Stovall, Kevin
Susobhanan, Abhimanyu
Swiggum, Joseph K.
Taylor, Stephen R.
Turner, Jacob E.
Unal, Caner
Vallisneri, Michele
van Haasteren, Rutger
Vigeland, Sarah J.
Wahl, Haley M.
Witt, Caitlin A.
Young, Olivia
Source :
ApJ 959 9 (2023)
Publication Year :
2023

Abstract

Analysis of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nHz frequency band. The most plausible source of such a background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for such a background and assess its significance make several simplifying assumptions, namely: i) Gaussianity; ii) isotropy; and most often iii) a power-law spectrum. However, a stochastic background from a finite collection of binaries does not exactly satisfy any of these assumptions. To understand the effect of these assumptions, we test standard analysis techniques on a large collection of realistic simulated datasets. The dataset length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15-year dataset. Simulated signals from millions of binaries drawn from models based on the Illustris cosmological hydrodynamical simulation were added to the data. We find that the standard statistical methods perform remarkably well on these simulated datasets, despite their fundamental assumptions not being strictly met. They are able to achieve a confident detection of the background. However, even for a fixed set of astrophysical parameters, different realizations of the universe result in a large variance in the significance and recovered parameters of the background. We also find that the presence of loud individual binaries can bias the spectral recovery of the background if we do not account for them.<br />Comment: 14 pages, 8 figures, version matching published paper

Details

Database :
arXiv
Journal :
ApJ 959 9 (2023)
Publication Type :
Report
Accession number :
edsarx.2309.04443
Document Type :
Working Paper
Full Text :
https://doi.org/10.3847/1538-4357/ad09e4