Nonlinear Decline‐Rate Dependence and Intrinsic Variation of Type Ia Supernova Luminosities
Bibliographic record
Abstract
Published B and V fluxes from nearby Type Ia supernovae (SNe) are fitted to light-curve templates with four to six adjustable parameters. Separately, B magnitudes from the same sample are fitted to a linear dependence on B - V color within a postmaximum time window prescribed by the CMAGIC method. These fits yield two independent SN magnitude estimates B max and B BV . Their difference varies systematically with decline-rate Δ m 15 in a form that is compatible with a bilinear but not a linear dependence; a nonlinear form likely describes the decline-rate dependence of B max itself. A Hubble fit to the average of B max and B BV requires a systematic correction for observed B - V color that can be described by a linear coefficient = 2.59 ± 0.24, well below the coefficient R B ≈ 4.1 commonly used to characterize the effects of Milky Way dust. At 99.9% confidence the data reject a simple model in which no color correction is required for SNe that are clustered at the blue end of their observed color distribution. After systematic corrections are performed, B max and B BV exhibit mutual rms intrinsic variation equal to 0.074 ± 0.019 mag, of which at least an equal share likely belongs to B BV . SN magnitudes measured using maximum luminosity or CMAGIC methods show comparable rms deviations of order ≈0.14 mag from the Hubble line. The same fit also establishes a 95% confidence upper limit of 486 km s -1 on the rms peculiar velocity of nearby SNe relative to the Hubble flow.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".