Constraining primordial black hole dark matter with CHIME fast radio bursts
Bibliographic record
Abstract
Strong lensing of fast radio bursts (FRBs) has been proposed as a relatively clean probe of primordial black hole (PBH) dark matter. Recently, the Canadian Hydrogen Intensity Mapping Experiment (CHIME) published a first catalog of 536 FRBs, 62 of which are from repeating sources. In light of this new data, we reexamine the prospects to constrain the abundance of PBHs via FRBs. Extending previous forecasts, we calculate a PBH dark matter bound using the intrinsic burst width and a calibrated flux-ratio threshold per FRB. In addition, we take into account the uncertainty in the relation between the FRB dispersion measure and source redshift. We outline an algorithm to detect lensed FRBs and a method to simulate its performance on real data and set a flux-ratio threshold for each event, which we use to infer realistic forecasts. We then attempt to extract a preliminary bound using the publicly available CHIME data. Unfortunately, both instrumental noise and the provided $\ensuremath{\sim}1\text{ }\text{ }\mathrm{ms}$ time-resolution of the public data hinder this effort. We identify one candidate event where a double burst could be explained via strong lensing by a $\mathcal{O}(10\text{ }\text{ }{M}_{\ensuremath{\bigodot}})$-mass PBH, which will require follow-up study at higher time resolution to either confirm or discard. We show that with a few times the size of the first catalog---sampled at the full instrumental time-resolution so that candidates can be efficiently scrutinized---CHIME will be able to find strong evidence for or robustly rule out PBHs with mass above $\ensuremath{\sim}10\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ as the dark matter. Finally, we demonstrate that stacking repeating FRBs can improve the constraints, especially for lower masses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".