Constraints on Fast Radio Burst Population from the First CHIME/FRB Catalog with the Hierarchical Bayesian Inference
Bibliographic record
Abstract
Abstract Fast radio bursts (FRBs) have emerged as one of the most dynamic areas of research in astronomy and cosmology. Despite increasing number of FRBs having been reported, the exact origin of FRBs remains elusive. Investigating the intrinsic redshift distributions of FRBs could provide valuable insights into their possible origins and enhance the power of FRBs as a cosmological probe. In this paper, we propose a hierarchical Bayesian inference approach combining with several viable models to investigate the redshift distribution of the Canadian Hydrogen Intensity Mapping Experiment/FRB catalog 1. By utilizing this method, we aim to uncover the underlying patterns and characteristics of the FRB population, i.e., the intrinsic redshift distribution of FRB. Taking uncertainties within the observational data and selection effects into consideration, we found that the redshift distribution of FRBs is significantly delayed with respect to that of the star formation history.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".