Probing gravitational slip with strongly lensed fast radio bursts
Bibliographic record
Abstract
The rapid accumulation of observed fast radio bursts (FRBs) originating from cosmological distances makes it likely that some will be strongly lensed by intervening matter along the line of sight. Detection of lensed FRB repeaters, which account for a noteworthy fraction of the total population, will allow not only an accurate measurement of the lensing time delay, but also follow-up high-resolution observations to pinpoint the location of the lensed images. Recent works proposed to use such strongly lensed FRBs to derive constraints on the current expansion rate ${H}_{0}$ as well as on cosmic curvature. Here, we study the prospects for placing constraints on departures from general relativity via such systems. Using an ensemble of simulated events, we focus on the gravitational slip parameter ${\ensuremath{\gamma}}_{\mathrm{PN}}$ in screened modified gravity models and show that FRB time-delay measurements can yield constraints as tight as $|{\ensuremath{\gamma}}_{\mathrm{PN}}\ensuremath{-}1|\ensuremath{\lesssim}0.04\ifmmode\times\else\texttimes\fi{}(\mathrm{\ensuremath{\Lambda}}/100\text{ }\text{ }\mathrm{kpc})\ifmmode\times\else\texttimes\fi{}[N/10{]}^{\ensuremath{-}1/2}$ at $1\ensuremath{\sigma}$ with ten detections.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".