The type la supernova rate at z ≈ 0.5 from the supernova legacy survey
Bibliographic record
Abstract
We present a measurement of the distant Type la supernova (SN Ia) rate derived from the first 2 yr of the Canada-France-Hawaii Telescope Supernova Legacy Survey. We observed four 1° × 1° fields with a typical temporal frequency of (Δt) ∼ 4 observer-frame days over time spans of 158-211 days per season for each field, with breaks during the full Moon. We used 8-10 m class telescopes for spectroscopic follow-up to confirm our candidates and determine their redshifts. Our starting sample consists of 73 spectroscopically verified SNe la in the redshift range 0.2 < z < 0.6. We derive a volumetric SN Ia rate of r V(〈z〉 = 0.47) = [0.42 0.090.13(syst.) ± 0.06(stat.)] × 10 -4 yr -1 Mpc 3, assuming h = 0.7, Ω 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(〈z〉 = 0.47) = 0.154 -0.0330.048(syst.) 0.0310.039(stat.) SN units. Using our rate alone, we place an upper limit on the component of SN la production that tracks the cosmic star formation history of 1 SN la per 10 3 M ⊙ of stars formed. Our rate and other rates from surveys using spectroscopic sample confirmation display only a modest evolution out to z = 0.55. © 2006. The American Astronomical Society. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".