Spectral Sequences of Type Ia Supernovae. I. Connecting Normal and Subluminous SNe Ia and the Presence of Unburned Carbon
Bibliographic record
Abstract
Abstract Type Ia supernovae (SNe Ia) are generally agreed to arise from thermonuclear explosions of carbon–oxygen white dwarfs. The actual path to explosion, however, remains elusive, with numerous plausible parent systems and explosion mechanisms suggested. Observationally, SNe Ia have multiple subclasses, distinguished by their light curves and spectra. This raises the question of whether these indicate that multiple mechanisms occur in nature or that explosions have a large but continuous range of physical properties. We revisit the idea that normal and 91bg-like SNe can be understood as part of a spectral sequence in which changes in temperature dominate. Specifically, we find that a single ejecta structure is sufficient to provide reasonable fits of both the normal SN Ia SN 2011fe and the 91bg-like SN 2005bl, provided that the luminosity and thus temperature of the ejecta are adjusted appropriately. This suggests that the outer layers of the ejecta are similar, thus providing some support for a common explosion mechanism. Our spectral sequence also helps to shed light on the conditions under which carbon can be detected in premaximum SN Ia spectra—we find that emission from iron can “fill in” the carbon trough in cool SNe Ia. This may indicate that the outer layers of the ejecta of events in which carbon is detected are relatively metal-poor compared to events in which carbon is not detected.
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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 | 0.000 |
| 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".