Evidence for a luminosity–decay correlation in GRB GeV light curves
Bibliographic record
Abstract
ABSTRACT Correlations between intrinsic properties of gamma-ray burst (GRB) light curves provide clues to the nature of the central engine, the jet, and a possible means to standardize GRBs for cosmological use. Here, we report on the discovery of a correlation between the intrinsic early-time luminosity, LG,10 s, measured at rest frame 10 s, and the average decay rate measured from rest frame 10 s onward, $\alpha _{\mathrm{G,avg\gt 10\, s}}$, in a sample of 13 Fermi Large Area Telescope long GRB light curves. We note that our selection criteria, in particular the requirement for a redshift to construct luminosity light curves, naturally limits our sample to energetic GRBs. A Spearman’s rank correlation gives a coefficient of –0.74, corresponding to a confidence level of 99.6 per cent, indicating that brighter afterglows decay faster than less luminous ones. Assuming a linear relation with log(LG,10s), we find $\alpha _{\mathrm{G,avg\gt 10\, s}}$$= -0.31_{-0.09}^{+0.12}\log ($LG,10s$) + 14.43_{-5.97}^{+4.55}$. The slope of −0.31 is consistent at 1σ with previously identified correlations in the optical/ultraviolet and X-ray light curves. We speculate that differences in the rate at which energy is released by the central engine or differences in observer viewing angle may be responsible for the correlation.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".