Effect of evolving physical constants on type Ia supernova luminosity
Bibliographic record
Abstract
ABSTRACT Type Ia supernovae, SNeIa, are used as standard candles in cosmology to determine the distances of the galaxies harbouring them. We show that the luminosity of an SNIa depends on its distance from us when physical constants (the speed of light c, the gravitational constant G, and the Planck constant h) are permitted to evolve. It is because the Chandrasekhar mass of the white dwarf that explodes to create SNIa depends on the values of the constants at the epoch the SNIa is formed. We show that the SNeIa luminosities could be about four times higher in the past than they are now. Thus, the luminosity distance estimation of the earliest SNeIa could be off by up to a factor of 2. Cosmological parameters, determined with this correction applied to the redshift versus distance modulus data base (Pantheon SNeIa), are not very different from those from the standard ΛCDM model without this correction, except for the dark-energy density and the curvature energy density; the latter increases at the cost of the former. Variations of the constants are given by $\dot{G}/G = \ 3.90 \ ( { \pm 0.04} ) \times {10^{ - 10}}\ {\rm y{r^{ - 1}}}$and $\dot{c}/c = \dot{h}/h\ = \ 1.30\ ( { \pm 0.01} ) \times {10^{ - 10}}\ {\rm y{r^{ - 1}}}$ at present. These variations are valid only when $G,\ c,\ $and$\ h$ are permitted to vary concurrently rather than individually.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".