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arxiv: 2408.10166 · v2 · pith:GBIXLLC3 · submitted 2024-08-19 · astro-ph.HE · astro-ph.CO· gr-qc· hep-ph

The NANOGrav 15 yr Data Set: Running of the Spectral Index

Gabriella Agazie , Akash Anumarlapudi , Anne M. Archibald , Zaven Arzoumanian , Jeremy George Baier , Paul T. Baker , Bence B\'ecsy , Laura Blecha
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Adam Brazier Paul R. Brook Sarah Burke-Spolaor J. Andrew Casey-Clyde Maria Charisi Shami Chatterjee Tyler Cohen James M. Cordes Neil J. Cornish Fronefield Crawford H. Thankful Cromartie Kathryn Crowter Megan E. DeCesar Paul B. Demorest Heling Deng Lankeswar Dey Timothy Dolch David Esmyol Elizabeth C. Ferrara William Fiore Emmanuel Fonseca Gabriel E. Freedman Emiko C. Gardiner Nate Garver-Daniels Peter A. Gentile Kyle A. Gersbach Joseph Glaser Deborah C. Good Kayhan G\"ultekin Jeffrey S. Hazboun Ross J. Jennings Aaron D. Johnson Megan L. Jones David L. Kaplan Luke Zoltan Kelley Matthew Kerr Joey S. Key Nima Laal Michael T. Lam William G. Lamb Bjorn Larsen T. Joseph W. Lazio Natalia Lewandowska Rafael R. Lino dos Santos 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 Patrick M. Meyers Chiara M. F. Mingarelli 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 Jessie C. Runnoe Alexander Saffer Shashwat C. Sardesai Ann Schmiedekamp Carl Schmiedekamp Kai Schmitz Tobias Schr\"oder Brent J. Shapiro-Albert Xavier Siemens Joseph Simon Magdalena S. Siwek Sophia V. Sosa Fiscella 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 Richard von Eckardstein Haley M. Wahl Caitlin A. Witt David Wright Olivia Young
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keywords modelbetadatananogravspectralgammaindexminimal
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The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA.

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