REVIEW 3 cited by
Sound speed resonance of the stochastic gravitational wave background
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We propose a novel mechanism to test time variation of the propagation speed of gravitational waves (GWs) in light of GWs astronomy. As the stochastic GWs experience the whole history of cosmic expansion, they encode potential observational evidence of such variation. We report that, one feature of a varying GWs speed is that the energy spectrum of GWs will present resonantly-enhanced peaks if the GWs speed oscillates in time at high-energy scales. Such oscillatory behaviour arises in a wide class of modified gravity theories. The amplitude of these peaks can be at reach by current and forthcoming GWs instruments, hence making the underlying theories falsifiable. This mechanism reveals that probing the variation of GWs speed can be a promising way to search for new physics beyond general relativity.
Forward citations
Cited by 3 Pith papers
-
Atomic Quantum Sensors for High-Frequency Gravitational Wave Searches
A cavity-plus-atomic-sensor design could reach strain sensitivities down to ~1e-37 Hz^-1/2 in aggressive optical configurations, opening the unexplored high-frequency gravitational-wave band.
-
Can tensor-scalar induced GWs dominate PTA observations ?
A Bayesian fit to NANOGrav 15-year data finds that tensor-scalar induced gravitational waves plus primordial tensor waves can fit the PTA background, with amplitudes constrained by CMB, BAO, and PBH limits.
-
Tensor induced gravitational waves
Second-order tensor-induced gravitational waves can shift the inferred parameters of small-scale primordial gravitational wave models fitted to NANOGrav 15-year data, with one model favored by Bayes factors.
Discussion (0). Continue with ORCID to comment.