The ESO/VLT 3rd year Type Ia supernova data set from the Supernova Legacy Survey
Bibliographic record
Abstract
We present 139 spectra of 124 Type Ia supernovae (SNeIa) that were observed at the ESO/VLT during the first three years of the Canada-France-Hawai Telescope (CFHT) Supernova Legacy Survey (SNLS). This homogeneous data set is used to test for redshift evolution of SNeIa spectra, and will be used in the SNLS 3rd year cosmological analyses. Spectra have been reduced and extracted with a dedicated pipeline that uses photometric information from deep CFHT Legacy Survey (CFHT-LS) reference images to trace, at sub-pixel accuracy, the position of the supernova on the spectrogram as a function of wavelength. It also separates the supernova and its host light in 60% of cases. The identification of the supernova candidates is performed using a spectrophotometric SNIa model. A total of 124 SNeIa, roughly 50% of the overall SNLS spectroscopic sample, have been identified using the ESO/VLT during the first three years of the survey. Their redshifts range from z=0.149 to z=1.031. The average redshift of the sample is z=0.63+/-0.02. This constitutes the largest SNIa spectral set to date in this redshift range. The spectra are presented along with their best-fit spectral SNIa model and a host model where relevant. In the latter case, a host subtracted spectrum is also presented. We produce average spectra for pre-maximum, maximum and post-maximum epochs for both z<0.5 and z>=0.5 SNeIa. We find that z<0.5 spectra have deeper intermediate mass element absorptions than z>= 0.5 spectra. The differences with redshift are consistent with the selection of brighter and bluer supernovae at higher redshift.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.005 | 0.003 |
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".