Spectral Sequences of Type Ia Supernovae. II. Carbon as a Diagnostic Tool for Explosion Mechanisms
Bibliographic record
Abstract
Abstract How an otherwise inert carbon–oxygen white dwarf can be made to explode as an SN Ia remains unknown. A promising test of theoretical models is to constrain the distribution of material that is left unburned, carbon in particular. So far, most investigations have used line identification codes to detect carbon in the ejecta, a method that cannot be readily compared against model predictions because it requires assumed opacities and temperatures. Here, we instead use tomographic techniques to investigate the amount of carbon in the inner layers of SN 2011fe, starting from the previously published tomographic analysis of Mazzali et al. From the presence of the carbon feature in the optical at early epochs and its disappearance later on, we derive an average carbon mass fraction between 0.001 and 0.05 for velocities in the range , and an upper limit of 0.005 inside that region. Based on our models and the assumed density profile, only small amounts of carbon should be in the neutral state, too little to be responsible for features seen in near-infrared spectra that were previously identified as due to neutral carbon. We discuss possible reasons for this discrepancy and compare our results against a suite of explosion models, although uncertainties in both the models and our simulations make it difficult to draw definitive conclusions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".