Stellar Mass–Dispersion Measure Correlations Constrain Baryonic Feedback in Fast Radio Burst Host Galaxies
Bibliographic record
Abstract
Abstract Low-redshift fast radio bursts (FRBs) enable robust measurements of the host galaxy contribution to the dispersion measure (DM), offering valuable constraints on the circumgalactic medium (CGM) of FRB hosts. We curate a sample of 20 nearby FRBs with low scattering timescales and face-on host galaxies with stellar masses in the range 10 9 < M * / M ⊙ < 10 11 . We fit the distribution of the host galaxy DM to a quadratic model as a function of stellar mass with a mass-independent scatter and find that the more massive the host, the lower its host DM. We report that this relation has a negative slope of m = −97 ± 44 pc cm −3 dex −1 in stellar mass. We compare this measurement against similar fits to three subgrid models implemented in the CAMELS suite of simulations from Astrid, IllustrisTNG, and SIMBA, which predict the CGM contribution to this relation, finding disagreement with the fiducial CAMELS-Astrid model, particularly for the most massive hosts ( M * > 10 10.5 M ⊙ ). More generally, models that attribute a positive correlation between stellar mass and host DM ( m > 0) to the CGM are in tension with our measurement unless compensated by fine-tuning of the host interstellar medium contribution as a function of stellar mass, e.g., at the low-mass end. We show that this conclusion is robust to a wide range of assumptions, such as the offset distribution of FRBs from their hosts and the statistics of the cosmic contribution to the DM budget along each sight line. Our results indirectly imply a lower limit on the strength of baryonic feedback in the local Universe ( z < 0.2) in isolated ∼ L * halos, complementing results from weak-lensing surveys and kSZ observations that target higher halo mass and redshift ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".