The NANOGrav 15yr Data Set: Search for Signals from New Physics

Adeela Afzal, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Bence Bécsy, Jose Juan Blanco-Pillado, Laura Blecha, Kimberly K. Boddy, Adam Brazier, Paul R. Brook, Sarah Burke-Spolaor, Rand Burnette, Robin Case, Maria Charisi, Shami Chatterjee, Katerina Chatziioannou, Belinda D. Cheeseboro, Siyuan ChenTyler Cohen, James M. Cordes, Neil J. Cornish, Fronefield Crawford, H. Thankful Cromartie, Kathryn Crowter, Curt J. Cutler, Megan E. DeCesar, Dallas DeGan, Paul B. Demorest, Heling Deng, Timothy Dolch, Brendan Drachler, Richard von Eckardstein, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Kyle A. Gersbach, Joseph Glaser, Deborah C. Good, Lydia Guertin, Kayhan Gültekin, Jeffrey S. Hazboun, Sophie Hourihane, Kristina Islo, Ross J. Jennings, Aaron D. Johnson, Megan L. Jones, Andrew R. Kaiser, Luke Zoltan Kelley, Matthew Kerr, Joey S. Key, Nima Laal, Michael T. Lam, David L. Kaplan, Joseph T.W. Lazio, Vincent S.H. Lee, Natalia Lewandowska, Rafael R. Lino dos Santos, Tyson B. Littenberg, William G. Lamb, Tingting Liu, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Chung Pei Ma, Alexander McEwen, James W. McKee, Dustin R. Madison, Maura A. McLaughlin, Natasha McMann, Bradley W. Meyers, Patrick M. Meyers, Chiara M.F. Mingarelli, Andrea Mitridate, Jonathan Nay, Priyamvada Natarajan, Cherry Ng, David J. Nice, Stella Koch Ocker, Ken D. Olum, Timothy T. Pennucci, Benetge B.P. Perera, Polina Petrov, Nihan S. Pol, Henri A. Radovan, Scott M. Ransom, Paul S. Ray, Joseph D. Romano, Shashwat C. Sardesai, Ann Schmiedekamp, Carl Schmiedekamp, Kai Schmitz, Tobias Schröder, Levi Schult, Brent J. Shapiro-Albert, Xavier Siemens, Joseph Simon, Magdalena S. Siwek, Ingrid H. Stairs, Daniel R. Stinebring, Kevin Stovall, Peter Stratmann, Jerry P. Sun, Abhimanyu Susobhanan, Joseph K. Swiggum, Jacob Taylor, Stephen R. Taylor, Tanner Trickle, Jacob E. Turner, Michele Vallisneri, Caner Unal, Sonali Verma, Sarah J. Vigeland, Haley M. Wahl, Qiaohong Wang, Caitlin A. Witt, David Wright, Olivia Young, Kathryn M. Zurek

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The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.

Original languageEnglish (US)
Article numberacdc91
JournalAstrophysical Journal Letters
Issue number1
StatePublished - Jul 1 2023

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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