How general relativity stacks up: Testing gravity by coherently combining gravitational-wave observations
Year: 2026
Type: preprint
Abstract: A core mission of gravitational-wave astronomy is to test general relativity (GR) in the high-curvature, high-velocity regime. The field has already seen significant progress in this space, though a remaining challenge is how to combine data from a population of binary mergers. This can both boost the significance of potential GR violations, and develop a phenomenological picture of how deviations manifest in different mergers. We propose a framework to coherently combine gravitational-wave measurements to build a population-level test of physics beyond GR. We assume that deviations from GR are (i) strongest near the merger, (ii) vary smoothly with the total mass of the binary, and (iii) are most prominent in the $\ell=|m|=2$ mode. While these assumptions do not describe every possible deviation of GR, they provide us with a plausible starting point upon which a more general framework can be built. We apply our framework to 28 high signal-to-noise ratio binary black hole mergers from LIGO-Virgo-KAGRA's Fifth Gravitational-Wave Transient Catalog, finding no evidence for deviations from GR. Our framework allows one to show how deviations could vary as a function of total mass of the system. We argue that such a reconstruction can be used to convincingly distinguish between systematic waveform errors and genuine GR violations.
Source: arXiv (Cornell University)
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Australian Government, National Research Foundation, Ministry of Education, Culture, Sports, Science and Technology, Academia Sinica, Centre National de la Recherche Scientifique, Istituto Nazionale di Fisica Nucleare, Ministry of Science and ICT, South Korea, Science and Technology Facilities Council, Japan Society for the Promotion of Science
OpenAlex ID: W7223224220