<i>K</i>‐Corrections and Spectral Templates of Type Ia Supernovae
Why this work is in the frame
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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 broad-band 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 which incorporates a large and heterogeneous sample of observed spectra. We then remove the effects of broad-band 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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it