The ESO’s VLT type Ia supernova spectral set of the final two years of SNLS
Bibliographic record
Abstract
Aims. We aim to present 70 spectra of 68 new high-redshift type Ia supernovae (SNe Ia) measured at ESO’s VLT during the final two years of operation (2006–2008) of the Supernova Legacy Survey (SNLS). This new sample complements the VLT three year spectral set. Altogether, these two data sets form the five year sample of SNLS SN Ia spectra measured at the VLT on which the final SNLS cosmological analysis will partly be based. In the redshift range considered, this sample is unique in terms of homogeneity and number of spectra. We use it to investigate the possibility of a spectral evolution of SNe Ia populations with redshift as well as SNe Ia spectral properties as a function of lightcurve fit parameters and the mass of the host-galaxy. Methods. Reduction and extraction are based on both IRAF standard tasks and our own reduction pipeline. Redshifts are estimated from host-galaxy lines whenever possible or alternatively from supernova features. We used the spectro-photometric SN Ia model SALT2 combined with a set of galaxy templates that model the host-galaxy contamination to assess the type Ia nature of the candidates. Results. We identify 68 new SNe Ia with redshift ranging from z = 0.207 to z = 0.98 for an average redshift of z = 0.62. Each spectrum is presented individually along with its best-fit SALT2 model. Adding this new sample to the three year VLT sample of SNLS, the final dataset contains 209 spectra corresponding to 192 SNe Ia identified at the VLT. We also publish the redshifts of other candidates (host galaxies or other transients) whose spectra were obtained at the same time as the spectra of live SNe Ia. This list provides a new redshift catalog useful for upcoming galaxy surveys. Using the full VLT SNe Ia sample, we build composite spectra around maximum light with cuts in color, the lightcurve shape parameter (“stretch”), host-galaxy mass and redshift. We find that high-z SNe Ia are bluer, brighter and have weaker intermediate mass element absorption lines than their low-z counterparts at a level consistent with what is expected from selection effects. We also find a flux excess in the range [3000–3400] Å for SNe Ia in low mass host-galaxies (M < 1010M⊙) or with locally blue U–V colors, and suggest that the UV flux (or local color) may be used in future cosmological studies as a third standardization parameter in addition to stretch and color.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".