The ESO/VLT 3rd year Type Ia supernova data set from the supernova legacy survey
Bibliographic record
Abstract
Aims. We present 139 spectra of 124 Type Ia supernovae (SNe Ia) that were observed at the ESO/VLT during the first three years of the Canada-France-Hawaï Telescope (CFHT) supernova legacy survey (SNLS). This homogeneous data set is used to test for redshift evolution of SN Ia spectra, and will be used in the SNLS 3rd year cosmological analyses. Methods. 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 SN Ia model. Results. A total of 124 SNe Ia, 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 SN Ia spectral set to date in this redshift range. The spectra are presented along with their best-fit spectral SN Ia 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 SNe Ia. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".