The Type Ia Supernova Rate at z ~0.5 from the Supernova Legacy Survey
Bibliographic record
Abstract
We present a measurement of the distant Type Ia supernova rate derived from the first two years of the Canada -- France -- Hawaii Telescope Supernova Legacy Survey. We observed four one-square degree fields with a typical temporal frequency of ~ 4 observer-frame days over time spans of from 158 to 211 days per season for each field, with breaks during full moon. We used 8-10 meter-class telescopes for spectroscopic followup to confirm our candidates and determine their redshifts. Our starting sample consists of 73 spectroscopically verified Type Ia supernovae in the redshift range 0.2 < z < 0.6. We derive a volumetric SN Ia rate of r_V(=0.47) = 0.42^{+0.13}_{-0.09} (systematic) +- 0.06 (statistical) X 10^-4 yr^-1 Mpc^3, assuming h = 0.7, Omega_m = 0.3 and a flat cosmology. Using recently published galaxy luminosity functions derived in our redshift range, we derive a SN Ia rate per unit luminosity of r_L(=0.47) = 0.154^{+0.048}_{-0.033} (systematic) ^{+0.039}_{-0.031} (statistical) SNu. Using our rate alone, we place an upper limit on the component of SN Ia production that tracks the cosmic star formation history of 1 SN Ia per 10^3 M_sun of stars formed. Our rate and other rates from surveys using spectroscopic sample confirmation display only a modest evolution out to z=0.55.
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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 | 0.003 | 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) | 0.002 | 0.001 |
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".