TYPE IA SUPERNOVAE: COLORS, RATES, AND PROGENITORS
Bibliographic record
Abstract
ABSTRACT The rate of type Ia supernovae (SNe Ia) in a galaxy depends not only on stellar mass, but also on star formation history (SFH). Here we show that two simple observational quantities (g − r or u − r host galaxy color, and r-band luminosity), coupled with an assumed delay time distribution (DTD) (the rate of SNe Ia as a function of time for an instantaneous burst of star formation), are sufficient to accurately determine a galaxy’s SN Ia rate, with very little sensitivity to the precise details of the SFH. Using this result, we compare observed and predicted color distributions of SN Ia hosts for the MENeaCS cluster supernova survey, and for the SDSS Stripe 82 supernova survey. The observations are consistent with a continuous DTD, without any cutoff. For old progenitor systems, the power-law slope for the DTD is found to be . This result favors the double degenerate scenario for SN Ia, though other interpretations are possible. We find that the late-time slopes of the DTD are different at the 1σ level for low and high stretch supernova, which suggest a single degenerate (SD) scenario for the latter. However, due to ambiguity in the current models’ DTD predictions, SD progenitors can neither be confirmed as causing high stretch supernovae nor ruled out from contributing to the overall sample.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".