Refractive lensing of scintillating FRBs by subparsec cloudlets in the multiphase CGM
Bibliographic record
Abstract
We consider the refractive lensing effects of ionized cool ( T ∼ 10 4 K ) gas cloudlets in the circumgalactic medium (CGM) of galaxies. In particular, we discuss the combined effects of lensing from these cloudlets and scintillation from plasma screens in the Milky Way interstellar medium (ISM). We show that, if the CGM comprises a mist of subparsec cloudlets with column densities of order 10 17 cm − 2 (as predicted by [M. McCourt, S. P. Oh, R. O’Leary, A. M. Madigan, MNRAS 473 , 5407–5431 (2018)]), then fast radio bursts (FRBs) whose sightlines pass within a virial radius of a CGM halo will may be lensed into tens of refractive images with a ∼10 ms scattering timescale. When these images are formed, they will be resolved by scintillating screens in the Milky Way ISM and will suppress the observed scintillation. We illustrate this effect in refractive lensing and argue that positive detections of FRB scintillation may constrain the properties of these cool-gas cloudlets, with current scintillation observation weakly disfavoring the cloudlet model. We propose that sheet-like geometries for the cool gas in the CGM can reconcile quasar absorption measurements (from which we infer the presence of the cool gas with structure on subparsec scales) and the unexpected lack of lensing signals from this gas thus far observed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".