Measurement of Ω<sub><i>m</i></sub>, Ω<sub>Λ</sub>from a Blind Analysis of Type Ia Supernovae with CMAGIC: Using Color Information to Verify the Acceleration of the Universe
Bibliographic record
Abstract
We present measurements of Ω m and Ω Λ from a blind analysis of 21 high-redshift supernovae using a new technique (CMAGIC) for fitting the multicolor light curves of Type Ia supernovae, first introduced by Wang and coworkers. CMAGIC takes advantage of the remarkably simple behavior of Type Ia supernovae on color-magnitude diagrams and has several advantages over current techniques based on maximum magnitudes. Among these are a reduced sensitivity to host galaxy dust extinction, a shallower luminosity-width relation, and the relative simplicity of the fitting procedure. This allows us to provide a cross-check of previous supernova cosmology results, despite the fact that current data sets were not observed in a manner optimized for CMAGIC. We describe the details of our novel blindness procedure, which is designed to prevent experimenter bias. The data are broadly consistent with the picture of an accelerating universe and agree with a flat universe within 1.7 σ, including systematics. We also compare the CMAGIC results directly with those of a maximum magnitude fit to the same supernovae, finding that CMAGIC favors more acceleration at the 1.6 σ level, including systematics and the correlation between the two measurements. A fit for w assuming a flat universe yields a value that is consistent with a cosmological constant within 1.2 σ.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".