A Measurement of Circumgalactic Gas around Nearby Galaxies Using Fast Radio Bursts
Bibliographic record
Abstract
Abstract The distribution of gas in the circumgalactic medium (CGM) of galaxies of all types is poorly constrained. Foreground CGMs contribute an extra amount to the dispersion measure (DM) of fast radio bursts (FRBs). We measure this DM excess for the CGMs of 10 11 –10 13 M ⊙ halos using the CHIME/FRB first data release, a halo mass range that is challenging to probe in any other way. Because of the uncertainty in the FRBs’ angular coordinates, only for nearby galaxies is the localization sufficient to confidently associate them with intersecting any foreground halo. Thus we stack on galaxies within 80 Mpc, optimizing the stacking scheme to approximately minimize the stack’s variance and marginalize over uncertainties in FRB locations. The sample has 20–30 FRBs intersecting halos with masses of 10 11 –10 12 M ⊙ and also of 10 12 –10 13 M ⊙ , and these intersections allow a marginal 1 σ –2 σ detection of the DM excess in both mass bins. The bin of 10 11 –10 12 M ⊙ halos also shows a DM excess at 1–2 virial radii. By comparing data with different models for the CGM gas profile, we find that all models are favored by the data up to 2 σ level compared to the null hypothesis of no DM excess. With 3000 more bursts from a future CHIME data release, we project a 4 σ detection of the CGM. Distinguishing between viable CGM models by stacking FRBs with CHIME-like localization would require tens of thousands of bursts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".