Cosmological Aspects of Gamma‐Ray Bursts: Luminosity Evolution and an Estimate of the Star Formation Rate at High Redshifts
Bibliographic record
Abstract
Using 220 gamma-ray burst (GRB) redshifts and luminosities derived from the luminosity-variability relationship of Fenimore & Ramirez-Ruiz, we show that there exists a significant correlation between the GRB luminosity and redshift. In particular, we find that the evolution of the average luminosity can be parameterized as L ∝ (1 + z ) 1.4±~0.5 , where z is the burst redshift. We discuss the possible reasons behind this evolution and compare it with that of other known sources that exhibit similar behavior. In addition, we use nonparametric statistical techniques to independently estimate the distributions of the luminosity and redshift of bursts, accounting for the evolution (in contrast to previous studies, which have assumed that the luminosity function is independent of redshift). We present these distributions and discuss their implications. Most significantly, we find a comoving rate density of GRBs that continues to increase to (1 + z ) ≳ 10. From this estimate of the GRB rate density, we then use the population synthesis codes of Fryer et al. to estimate the star formation rate at high redshifts, for different progenitor models of GRBs. We find that no matter what the progenitor or population synthesis model, the star formation rate increases or remains constant to very high redshifts ( z ≳ 10).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".