Emergence hour-by-hour of <i>r</i>-process features in the kilonova AT2017gfo
Bibliographic record
Abstract
The spectral features in the optical/near-infrared counterparts of neutron star mergers (kilonovae, KNe) evolve dramatically on hourly timescales. To examine the spectral evolution, we compiled a temporal series that was complete at all observed epochs from 0.5 to 9.4 days of the best optical/near-infrared (NIR) spectra of the gravitational-wave detected kilonova AT2017gfo. Using our analysis of this spectral series, we show that the emergence times of spectral features place strong constraints on line identifications and ejecta properties, while their subsequent evolution probes the structure of the ejecta. We find that the most prominent spectral feature, the 1 μm P Cygni line, appears suddenly, with the earliest detection at 1.17 days. We find evidence in this earliest feature for the fastest yet discovered kilonova ejecta component at 0.40–0.45 c . Across the observed epochs and wavelengths, the velocities of the line-forming regions span nearly an order of magnitude, down to as low as 0.04–0.07 c . The time of emergence closely follows the predictions for Sr II because Sr III combines rapidly under local thermal equilibrium (LTE) conditions. The transition time between the doubly and singly ionised states provides the first direct measurement of the ionisation temperature. This temperature is highly consistent with the temperature of the emitted blackbody radiation field at a level of a few percent. Furthermore, we find the KN to be isotropic in temperature, that is, the polar and equatorial ejecta differ by less than a few hundred Kelvin or ≲5%, in the first few days post-merger based on measurements of the reverberation time-delay effect. This suggests that a model with very simple assumptions, with single-temperature LTE conditions, reproduces the early kilonova properties surprisingly well.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".