Bibliographic record
Abstract
Abstract The delay time distribution of Type Ia supernovae (SNe Ia; the time-dependent rate of SNe resulting from a burst of star formation) has been measured using different techniques and in different environments. Here we study in detail the distribution for field galaxies, using the SDSS DR7 Stripe 82 SN sample. We improve a technique we introduced earlier, which is based on galaxy color and luminosity and is insensitive to details of the star formation history, to include the normalization. Assuming a power-law dependence of the SN rate with time, DTD( t ) = A ( t /1 Gyr) s , we find a power-law index and a normalization , corresponding to a number of SNe Ia integrated over a Hubble time of . We also implement a method used by Maoz and collaborators, which is based on star formation history reconstruction, and find that this gives a consistent result for the slope but a lower, marginally inconsistent normalization. With our normalization, the distribution for field galaxies is made consistent with that derived for cluster galaxies. Comparing the inferred distribution with predictions from different evolutionary scenarios for SNe Ia, we find that our results are intermediate between the various predictions and do not yet constrain the evolutionary path leading to SNe Ia.
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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 | 0.003 |
| 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 | 0.000 |
| 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".