Search for Lensing Signatures from the Latest Fast Radio Burst Observations and Constraints on the Abundance of Primordial Black Holes
Bibliographic record
Abstract
Abstract The possibility that primordial black holes (PBHs) form some part of dark matter has been considered for a long time but poorly constrained over a wide mass range. Fast radio bursts (FRBs) are bright radio transients with millisecond duration. Their lensing effect has been proposed to be one of the cleanest probes for constraining the presence of PBHs in the stellar-mass window. In this paper, we first apply the normalized cross-correlation algorithm to search and identify candidates for lensed FRBs in the latest public FRB observations, i.e., 593 FRBs, which mainly consist of the first Canadian Hydrogen Intensity Mapping Experiment FRB catalog, and then derive constraints on the abundance of PBHs from the null search result of the lensing signature. For a monochromatic mass distribution, the fraction of dark matter made up of PBHs could be constrained to ≤87% for ≥500 M ⊙ at the 95% confidence level by assuming signal-to-noise ratios dependent on the flux ratio threshold for each FRB and that apparently one-off events are intrinsic single bursts. This result would be improved by a factor of 3 when a conventional constant flux ratio threshold is considered. Moreover, we derive constraints on PBHs with a log-normal mass function naturally predicted by some popular inflation models and often investigated with gravitational-wave detections. We find that, in this mass distribution scenario, the constraint from the current public FRB observations is relatively weaker than the one from gravitational-wave detections. It is foreseen that upcoming complementary multimessenger observations will yield considerable constraints on the possibilities of PBHs in this intriguing mass window.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".