Search arXivSearch

arXiv · 2309.04443

How to Detect an Astrophysical Nanohertz Gravitational-Wave Background

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-Clyde·Maria Charisi·Shami Chatterjee·Katerina Chatziioannou·Tyler Cohen·James M. Cordes·Fronefield Crawford·H. Thankful Cromartie·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. 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·Shashwat C. Sardesai·Ann Schmiedekamp·Carl Schmiedekamp·Kai Schmitz·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·Haley M. Wahl·Caitlin A. Witt·Olivia Young

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.

Explore related subjects

Keep this discovery

BibTeXRIS

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-Clyde, Maria Charisi, Shami Chatterjee, Katerina Chatziioannou, Tyler Cohen, James M. Cordes, Fronefield Crawford, H. Thankful Cromartie, 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. 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, Shashwat C. Sardesai, Ann Schmiedekamp, Carl Schmiedekamp, Kai Schmitz, 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, Haley M. Wahl, Caitlin A. Witt, Olivia Young. 2023-09-08. How to Detect an Astrophysical Nanohertz Gravitational-Wave Background. https://doi.org/10.3847/1538-4357/ad09e4

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Electrovacuum Black Hole Uniqueness

We prove the black hole uniqueness conjecture in the axially symmetric, stationary, electrovacuum setting, subject to the refined asymptotic analysis of the associated singular harmonic maps, which includes an analyticity hypothesis at the axes. More precisely, it is shown that any asymptotically flat solution of the Einstein--Maxwell equations in this class, with more than one black hole horizon component is either: Majumdar--Papapetrou, up to a duality rotation, in which case all logarithmic angle defects vanish, or every finite axis rod logarithmic angle defect is strictly negative and hence every interaction force is strictly attractive. The proof extends the singular harmonic map method used for vacuum Kerr uniqueness in [18].

gr-qc

Constraining Modified Mass-to-Horizon Cosmology Through Primordial Inflationary Observables

We investigate slow-roll inflation in a modified cosmological framework inspired by a generalized mass-to-horizon relation (MHR), $M=\gamma {c^2 L^n}/{G}$, where $n$ is a real parameter and $\gamma$ a dimensional constant. Using Padmanabhan's emergence paradigm, we derive the modified Friedmann equations for a flat FRW universe and analyze the dynamics of a canonical scalar field (inflaton) under the slow-roll approximation. We study the resulting inflationary phenomenology for power-law and Starobinsky potentials. For power-law potentials, the MHR modification fails to reconcile these models with current CMB constraints on $r$ and $n_s$. In contrast, Starobinsky inflation exhibits significant sensitivity to deviations from $n=1$. A perturbative analysis ($n=1+\Delta$) yields corrections to inflationary observables. We observe that the scalar power-spectrum normalization, under a fixed-Starobinsky prescription, imposes the stringent constraint $0.960 \lesssim n \lesssim 1.040$ for $N=60$ efolds. This is considerably tighter than spectral-index bounds. Our results establish inflation, particularly Starobinsky-like models, as a sensitive probe of generalized horizon thermodynamics and departures from standard MHR scaling.

gr-qc

Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction

In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.

gr-qc