Constraints on the Time Variation of the Gravitational Constant Using Gravitational Wave Observations of Binary Neutron Stars
Bibliographic record
Abstract
We propose a method to constrain the variation of the gravitational constant $G$ with cosmic time using gravitational wave (GW) observations of merging binary neutron stars. The method essentially relies on the fact that the maximum and minimum allowed masses of neutron stars at a particular cosmic epoch have a simple dependence on the value of $G$ at that epoch. GWs carry an imprint of the value of $G$ at the time of the merger. Thus, if the value of $G$ at merger is significantly different from its current value, the masses of the neutron stars inferred from the GW observations will be inconsistent with the theoretically allowed range. This enables us to place bounds on the variation of $G$ between the merger epoch and the present epoch. Using the observation of the binary neutron star system GW170817, we constrain the fractional difference in $G$ between the merger and the current epoch to be in the range $\ensuremath{-}1\ensuremath{\lesssim}\mathrm{\ensuremath{\Delta}}G/G\ensuremath{\lesssim}8$. Assuming a monotonic variation in $G$, this corresponds to a bound on the average rate of change of $\ensuremath{-}7\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}\ensuremath{\le}\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{G}/G\ensuremath{\le}5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8}\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}$ between these epochs. Future observations will put tight constraints on the deviation of $G$ over vast cosmological epochs not probed by other observations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".