Spectra of High-Redshift Type Ia Supernovae and a Comparison with Their Low-Redshift Counterparts
Bibliographic record
Abstract
We present spectra for 14 high-redshift (0.17 < z < 0.83) supernovae, which were discovered by the Supernova Cosmology Project as part of a campaign to measure cosmological parameters. The spectra are used to determine the redshift and classify the supernova type, essential information if the supernovae are to be used for cosmological studies. Redshifts were derived either from the spectrum of the host galaxy or from the spectrum of the supernova itself. We present evidence that these supernovae are of Type Ia (SNe Ia) by matching to spectra of nearby supernovae. We find that the dates of the spectra relative to maximum light determined from this fitting process are consistent with the dates determined from the photometric light curves, and, moreover, the spectral time sequences for SNe Ia at low and high redshift are indistinguishable. We also show that the expansion velocities measured from blueshifted Ca H and K are consistent with those measured for low-redshift SNe Ia. From these first-level quantitative comparisons we find no evidence for evolution in SN Ia properties between these low- and high-redshift samples. Thus, even though our samples may not be complete, we conclude that there is a population of SNe Ia at high redshift whose spectral properties match those at low 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".