Forecasting the Fast Radio Burst Population Observed through Galaxy Cluster Lenses
Bibliographic record
Abstract
Abstract High-redshift fast radio bursts (FRBs) are expected to be extremely powerful probes of our Universe. However, while a significant number of FRBs are expected to exist at high redshift, detecting them has been difficult, with only a handful robustly confirmed at redshifts greater than one. In many other fields, gravitational lensing from galaxy clusters has enabled high-redshift detections by magnifying background sources. In this work we forecast the populations of FRBs expected to be detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and upcoming instrument Canadian Hydrogen Observatory and Radio Transient Detector (CHORD), for blank fields and by lensing through a range of strong lensing galaxy clusters, based on existing, observationally driven cluster models. We find that the presence of a galaxy cluster of mass M ≥ 5 × 10 14 M ⊙ within the detection beam of a transit telescope will approximately double the rate of detected high-redshift ( z ≥ 1 CHIME, z ≥ 2 CHORD) FRBs for that beam. Consequently, we find that knowledge of cluster positions can be used by instruments like CHIME or CHORD in tandem with novel observational strategies to isolate a sample of high-redshift FRBs with ≳50% purity at a rate of ≲3 per year. This would provide a statistically high-redshift sample of mostly gravitationally lensed FRBs that would be ideal candidates for optical follow-up, constraining the FRB–star formation relation and for use in cosmological studies including measuring H 0 , characterising dark matter substructures and probing reionization.
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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 | 0.002 |
| 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".