Cosmic Type Ia supernova rate and constraints on supernova Ia progenitors
Bibliographic record
Abstract
Context. Type Ia supernovae play a key role in the evolution of galaxies by polluting the interstellar medium with a fraction of iron peak elements larger than that released in the core-collapse supernova events. Their light curve, moreover, is widely used in cosmological studies as it constitutes a reliable distance indicator on extragalactic scales. Among the mechanisms proposed to explain the Type Ia supernovae (SNe), the single- and double-degenerate channels are thought to be the dominant ones, which implies a different distribution of time delays between the progenitor formation and the explosion. Aims. In this paper, we aim to determine the dominant mechanism by comparing a compilation of Type Ia SN rates with those computed from various cosmic star-formation histories coupled with different delay-time distribution functions. We also evaluate the relative contributions of both channels. Methods. By using a least-squares fitting procedure, we modeled the observations of Type Ia SN rates assuming different combinations of three recent cosmic star-formation rates and seven delay-time distributions. The goodness of these fits are statistically quantified by the χ 2 test. Results. For two of the three cosmic star-formation rates, the single degenerate scenario provides the most accurate explanation for the observations, while a combination of 34% single-degenerate- and 66% double-degenerate delay-time distributions is more plausible for the remaining tested cosmic star-formation rates. Conclusions. Though dependent on the assumed cosmic star-formation rate, we find arguments in favor of the single-degenerate model. From the theoretic point of view, at least ∼34% of the Type Ia SN must have been produced through the single-degenerate channel to account for the observations. The wide, double-degenerate mechanism slightly under-predicts the observations at redshift z ≳ 1, unless the cosmic SFR flattens in that regime. On the contrary, although the purely close double-degenerate scenario can be ruled out, we cannot rule out a mixed scenario with single- and double-degenerate progenitors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".