Scintillated microlensing: Measuring cosmic distances with fast radio bursts
Bibliographic record
Abstract
We propose a novel means of directly measuring cosmological distances using scintillated microlensing of fast radio bursts (FRBs). In standard strong lensing measurements of cosmic expansion, the main source of systematic uncertainty lies in modeling the mass profile of galactic halos. Using extra-galactic stellar microlensing to measure the Hubble constant avoids this systematic uncertainty as the lens potential of microlenses depends only on a single parameter: the mass of the lens. FRBs, which may achieve nanosecond precision on lensing time delays, are well suited to precision measurements of stellar microlensing, for which the time delays are on the order of milliseconds. However, typical angular separations between the microlensed images on the order of microarcseconds make the individual images impossible to spatially resolve with ground-based telescopes. We propose leveraging scintillation in the interstellar medium (ISM) to resolve the microlensed images, effectively turning the ISM into an astrophysical-scale interferometer. Using this technique, we estimate a 6% uncertainty on ${H}_{0}$ from a single observed scintillated microlensing event, with a subpercent uncertainty on ${H}_{0}$ achievable with only 30 such events. With an optical depth for stellar microlensing of ${10}^{\ensuremath{-}3}$, this may be achievable in the near future with upcoming FRB telescopes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".