Cosmology with gravitationally lensed repeating fast radio bursts
Bibliographic record
Abstract
High-precision cosmological probes have revealed a small but significant tension between the parameters measured with different techniques, among which there is one based on time delays in gravitational lenses. We discuss a new way of using time delays for cosmology, taking advantage of the extreme precision expected for lensed fast radio bursts, which are short flashes of radio emission originating at cosmological distances. With coherent methods, the achievable precision is sufficient for measuring how time delays change over the months and years, which can also be interpreted as differential redshifts between the images. It turns out that uncertainties arising from the unknown mass distribution of gravitational lenses can be eliminated by combining time delays with their time derivatives. Other effects, most importantly relative proper motions, can be measured accurately and disentangled from the cosmological effects. With a mock sample of simulated lenses, we show that it may be possible to attain strong constraints on cosmological parameters. Finally, the lensed images can be used as galactic interferometer to resolve structures and motions of the burst sources with incredibly high resolution and help reveal their physical nature, which is currently unknown.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".