A Type II Supernova Hubble Diagram from the CSP-I, SDSS-II, and SNLS Surveys*
Bibliographic record
Abstract
Abstract The coming era of large photometric wide-field surveys will increase the detection rate of supernovae by orders of magnitude. Such numbers will restrict spectroscopic follow-up in the vast majority of cases, and hence new methods based solely on photometric data must be developed. Here, we construct a complete Hubble diagram of Type II supernovae (SNe II) combining data from three different samples: the Carnegie Supernova Project-I, the Sloan Digital Sky Survey II SN, and the Supernova Legacy Survey. Applying the Photometric Color Method (PCM) to 73 SNe II with a redshift range of 0.01–0.5 and with no spectral information, we derive an intrinsic dispersion of 0.35 mag. A comparison with the Standard Candle Method (SCM) using 61 SNe II is also performed and an intrinsic dispersion in the Hubble diagram of 0.27 mag, i.e., 13% in distance uncertainties, is derived. Due to the lack of good statistics at higher redshifts for both methods, only weak constraints on the cosmological parameters are obtained. However, assuming a flat universe and using the PCM, we derive the universe’s matter density: providing a new independent evidence for dark energy at the level of two sigma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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 | 0.000 |
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".