The Carnegie Supernova Project: Absolute Calibration and the Hubble Constant
Bibliographic record
Abstract
Abstract We present an analysis of the final data release of the Carnegie Supernova Project I , focusing on the absolute calibration of the luminosity–decline rate relation for Type Ia supernovae (SNe Ia) using new intrinsic color relations with respect to the color-stretch parameter, s BV , enabling improved dust extinction corrections. We investigate to what degree the so-called fast-declining SNe Ia can be used to determine accurate extragalactic distances. We estimate the intrinsic scatter in the luminosity–decline rate relation and find it ranges from ±0.13 mag to ±0.18 mag with no obvious dependence on wavelength. Using the Cepheid variable star data from the SH0ES project, the SN Ia distance scale is calibrated and the Hubble constant is estimated using our optical and near-infrared sample, and these results are compared to those determined exclusively from a near-infrared subsample. The systematic effect of the supernova’s host galaxy mass is investigated as a function of wavelength and is found to decrease toward redder wavelengths, suggesting this effect may be due to dust properties of the host. Using estimates of the dust extinction derived from optical and near-infrared wavelengths and applying these to the H band, we derive a Hubble constant , whereas using a simple B − V color correction applied to the B band yields . Photometry of two calibrating SNe Ia from the CSP-II sample, SN 2012ht and SN 2015F, is presented and used to improve the calibration of the SN Ia distance ladder.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".