Predicted and Observed Evolution in the Mean Properties of Type Ia Supernovae with Redshift
Bibliographic record
Abstract
Recent studies indicate that Type Ia supernovae (SNe Ia) consist of two groups—a "prompt" component whose rates are proportional to the host galaxy star formation rate, whose members have broader light curves and are intrinsically more luminous, and a "delayed" component whose members take several Gyr to explode, have narrower light curves, and are intrinsically fainter. As cosmic star formation density increases with redshift, the prompt component should begin to dominate. We use a two-component model to predict that the average light curve width should increase by 6% from z = 0 to 1.5. Using data from various searches, we find an 8.1% ± 2.7% increase in average light curve width for non-subluminous SNe Ia from z = 0.03 to 1.12, corresponding to an increase in the average intrinsic luminosity of 12%. To test whether there is any bias after supernovae are corrected for light curve shape we use published data to mimic the effect of population evolution and find no significant difference in the measured dark energy equation of state parameter, w . However, future measurements of time-variable w will require standardization of SN Ia magnitudes to 2% up to z = 1.7, and it is not yet possible to assess whether light curve shape correction works at this level of precision. Another concern at z = 1.5 is the expected order-of-magnitude increase in the number of SNe Ia that cannot be calibrated by current methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".