<i>K</i>‐Corrections and Spectral Templates of Type Ia Supernovae
Bibliographic record
Abstract
With the advent of large dedicated Type Ia supernova (SN Ia) surveys, K -corrections of SNe Ia and their uncertainties have become especially important in the determination of cosmological parameters. While K -corrections are largely driven by SN Ia broadband colors, it is shown here that the diversity in spectral features of SNe Ia can also be important. For an individual observation, the statistical errors from the inhomogeneity in spectral features range from 0.01 (where the observed and rest-frame filters are aligned) to 0.04 (where the observed and rest-frame filters are misaligned). To minimize the systematic errors caused by an assumed SN Ia spectral energy distribution (SED), we outline a prescription for deriving a mean spectral template time series that incorporates a large and heterogeneous sample of observed spectra. We then remove the effects of broadband colors and measure the remaining uncertainties in the K -corrections associated with the diversity in spectral features. Finally, we present a template spectroscopic sequence near maximum light for further improvement on the K -correction estimate. A library of ~600 observed spectra of ~100 SNe Ia from heterogeneous sources is used for the analysis.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".