EVOLUTION IN THE VOLUMETRIC TYPE Ia SUPERNOVA RATE FROM THE SUPERNOVA LEGACY SURVEY
Bibliographic record
Abstract
We present a measurement of the volumetric Type Ia supernova (SN Ia) rate (SNR Ia ) as a function of redshift for the first four years of data from the Canada–France–Hawaii Telescope Supernova Legacy Survey (SNLS). This analysis includes 286 spectroscopically confirmed and more than 400 additional photometrically identified SNe Ia within the redshift range 0.1 ⩽ z ⩽ 1.1. The volumetric SNR Ia evolution is consistent with a rise to z ∼ 1.0 that follows a power law of the form (1+ z ) α , with α = 2.11 ± 0.28. This evolutionary trend in the SNLS rates is slightly shallower than that of the cosmic star formation history (SFH) over the same redshift range. We combine the SNLS rate measurements with those from other surveys that complement the SNLS redshift range, and fit various simple SN Ia delay-time distribution (DTD) models to the combined data. A simple power-law model for the DTD (i.e., ∝ t −β ) yields values from β = 0.98 ± 0.05 to β = 1.15 ± 0.08 depending on the parameterization of the cosmic SFH. A two-component model, where SNR Ia is dependent on stellar mass ( M stellar ) and star formation rate (SFR) as SNR Ia ( z ) = A × M stellar ( z ) + B × SFR( z ), yields the coefficients A = (1.9 ± 0.1) × 10 −14 SNe yr −1 M −1 ☉ and B = (3.3 ± 0.2) × 10 −4 SNe yr −1 ( M ☉ yr −1 ) −1 . More general two-component models also fit the data well, but single Gaussian or exponential DTDs provide significantly poorer matches. Finally, we split the SNLS sample into two populations by the light-curve width (stretch), and show that the general behavior in the rates of faster-declining SNe Ia (0.8 ⩽ s < 1.0) is similar, within our measurement errors, to that of the slower objects (1.0 ⩽ s < 1.3) out to z ∼ 0.8.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".