Electron-impact Excitation of Pt i–iii: The Importance of Metastables and Collision Processes in Neutron Star Merger and Laboratory Plasmas
Bibliographic record
Abstract
Abstract The detection of a gravitational-wave signal and subsequent electromagnetic transient from a neutron star merger in 2017 is consistent with expectations of neutron star mergers as an r-process element production site. Within the first few days post-merger, the kilonova spectra are consistent with a blackbody illuminating a mix of heavy, r-process elements. With increasing time, the kilonova transitions to the non-LTE regime where the level populations and ionization balance are determined by both collisional and photoprocesses. Detailed cross section data for electron-impact processes involving the relevant species are often not available. In such circumstances, it is reasonable to use approximate methods as baseline data for use in spectral modeling, and it is useful to evaluate the accuracy of such methods against more sophisticated collision calculations when possible. We describe new calculations of the electron-impact excitation cross sections of Pt i–iIi using the DARC R-matrix codes. Using collisional-radiative models, we show that, at plasma conditions expected in kilonovae, the expressions of van Regemorter and Axelrod are insufficient for producing electron-impact excitation data for complex, heavy species such as the low charge states of Pt. Through comparisons with data generated with the relativistic distorted wave approach, as implemented in the Flexible Atomic Code, we show the distorted wave method produces cross section data that, when incorporated into spectral models, predicts strong spectral feature distributions similar in intensity to those from models built on data computed with the R-matrix approach for the considered ions and plasma conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".