How to Detect an Astrophysical Nanohertz Gravitational Wave Background

• DOI: 10.3847/1538-4357/ad09e4
• Publication Date: 2023-11-30T22:47:11Z
• Authors: Bence Bécsy; Neil J. Cornish; Patrick M. Meyers; Luke Zoltan Kelley; Gabriella Agazie; Akash Anumarlapudi; Anne M. Archibald; Zaven Arzoumanian; Paul T. Baker; Laura Blecha; Adam Brazier; Paul R. Brook; Sarah Burke-Spolaor; J. Andrew Casey-C...; Maria Charisi; Shami Chatterjee; Katerina Chatziio...; Tyler Cohen; James M. Cordes; Fronefield Crawford; H. Thankful Croma...; Kathryn Crowter; Megan E. DeCesar; Paul B. Demorest; Timothy Dolch; Elizabeth C. Ferrara; William Fiore; Emmanuel Fonseca; Gabriel E. Freedman; Nate Garver-Daniels; Peter A. Gentile; Joseph Glaser; Deborah C. Good; Kayhan Gültekin; Jeffrey S. Hazboun; Sophie Hourihane; Ross J. Jennings; Aaron D. Johnson; Megan L. Jones; Andrew R. Kaiser; David L. Kaplan; Matthew Kerr; Joey S. Key; Nima Laal; Michael T. Lam; William G. Lamb; T. Joseph W. Lazio; Natalia Lewandowska; Tyson B. Littenberg; Tingting Liu; Duncan R. Lorimer; Jing Luo; Ryan S. Lynch; Chung-Pei Ma; Dustin R. Madison; Alexander McEwen; James W. McKee; Maura A. McLaughlin; Natasha McMann; Bradley W. Meyers; Chiara M. F. Ming...; Andrea Mitridate; Cherry Ng; David J. Nice; Stella Koch Ocker; Ken D. Olum; Timothy T. Pennucci; Benetge B. P. Perera; Nihan S. Pol; Henri A. Radovan; Scott M. Ransom; Paul S. Ray; Joseph D. Romano; Shashwat C. Sardesai; Ann Schmiedekamp; Carl Schmiedekamp; Kai Schmitz; Brent J. Shapiro-...; Xavier Siemens; Joseph Simon; Magdalena S. Siwek; Sophia V. Sosa Fi...; Ingrid H. Stairs; Daniel R. Stinebring; Kevin Stovall; Abhimanyu Susobhanan; Joseph K. Swiggum; Stephen R. Taylor; Jacob E. Turner; Caner Unal; Michele Vallisneri; Rutger van Haasteren; Sarah J. Vigeland; Haley M. Wahl; Caitlin A. Witt; Olivia Young
• Keywords: binary: mass; noise; gravitational radiation: stochastic; FOS: Physical sciences; General Relativity and Quantum Cosmology (gr-qc); Bayesian statistics; Astrophysics; 530; General Relativity and Quantum Cosmology; Gravitational waves; statistical analysis; cosmological model: parameter space; Supermassive black holes; pulsar; Millisecond pulsars; Gravitational wave sources; High Energy Astrophysical Phenomena (astro-ph.HE); superposition; gravitational radiation: background; 500; Astronomy and Astrophysics; NANOGrav; background: stochastic; QB460-466; black hole: binary; Space and Planetary Science; hydrodynamics; Astronomy data analysis; spectral; Gravitational wave astronomy; Astrophysics - High Energy Astrophysical Phenomena; info:eu-repo/classification/ddc/520

Abstract Analyses of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nanohertz frequency band. The most plausible source of this background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for this 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 data sets. The data-set length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15 yr data set. 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 data sets, even though their fundamental assumptions are not 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.