Quantitative comparison between type Ia supernova spectra at low and high redshifts: a case study
Bibliographic record
Abstract
We develop a method to measure the strength of the absorption features in type Ia supernova (SN Ia) spectra and use it to make a quantitative comparisons between the spectra of type Ia supernovae at low and high redshifts. In this case study, we apply the method to 12 high-redshift (0.212 ≤ z ≤ 0.912) SNe Ia observed by the Supernova Cosmology Project. Through measurements of the strengths of these features and of the blueshift of the absorption minimum in Ca ii H&K, we show that the spectra of the high-redshift SNe Ia are quantitatively similar to spectra of nearby SNe Ia (z < 0.15). One supernova in our high redshift sample, SN 2002fd at z = 0.279, is found to have spectral characteristics that are associated with peculiar SN 1991T/SN 1999aa-like supernovae.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".