Spectroscopic r-process Abundance Retrieval for Kilonovae. II. Lanthanides in the Inferred Abundance Patterns of Multicomponent Ejecta from the GW170817 Kilonova
Bibliographic record
Abstract
Abstract In kilonovae, freshly synthesized r-process elements imprint features on optical spectra, as observed in AT2017gfo, the counterpart to the GW170817 binary neutron star merger. However, measuring the r-process compositions of the merger ejecta is computationally challenging. Vieira et al. introduced Spectroscopic r-process Abundance Retrieval for Kilonovae (SPARK), a software tool to infer elemental abundance patterns of the ejecta and associate spectral features with particular species. Previously, we applied SPARK to the 1.4-day spectrum of AT2017gfo and inferred its abundance pattern for the first time, characterized by electron fraction Y e = 0.31, a substantial abundance of strontium, and a dearth of lanthanides and heavier elements. This ejecta is consistent with wind from a remnant hypermassive neutron star and/or accretion disk. We now extend our inference to spectra at 2.4 and 3.4 days and test the need for multicomponent ejecta, where we stratify the ejecta in composition. The ejecta at 1.4 and 2.4 days is described by the same single blue component. At 3.4 days, a new redder component with lower Y e = 0.16 and a significant abundance of lanthanides emerges. This new redder component is consistent with dynamical ejecta and/or neutron-rich ejecta from a magnetized accretion disk. As expected from photometric modeling, this component emerges as the ejecta expands, the photosphere recedes, and the earlier bluer component dims. At 3.4 days, we find an ensemble of lanthanides, with the presence of cerium most concrete. This presence of lanthanides has important implications for the contribution of kilonovae to the r-process abundances observed in the Universe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".