Can Kilonova Light Curves Be Standardized?
Bibliographic record
Abstract
Abstract Binary neutron star (NS) mergers have been recently confirmed to be the progenitors of the optical transients kilonovae (KNe). KNe are powered by the radioactive decay of neutron-rich elements (r-process elements), which are believed to be the product of disruption of NSs during their merger. KNe exhibit interesting parallels with SNe Ia, whose light curves show specific correlations that allow them to be used as standardizable candles. In this Letter, we investigate whether KNe light curves could exhibit similar correlations. While a satisfactory answer to this question can only be provided by future KNe observations, employing theoretical models we explore whether there is any ground for harboring such expectations. Using semi-analytic models of KNe light curves in conjunction with results from numerical relativity simulations of binary NS mergers, we obtain the maximum bolometric luminosity ( ) and decline from peak luminosity ( ) for a simulated population of mergers. We find that theoretical light curves of KNe show remarkable correlations despite the complex physics governing their behavior. This presents a possibility of future observations to uncover such correlations in the observed light curves, eventually allowing observers to standardize these light curves and to use them for local distance measurements.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".