COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY
Bibliographic record
Abstract
We present the cosmological analysis of 752 photometrically–classified Type Ia Supernovae (SNe Ia) \nobtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented \nwith host–galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). \nOur photometric–classificationmethod is based on the SN typing technique of Sako et al. (2011), aided \nby host galaxy redshifts (0.05 < z < 0.55). SNANA simulations of our methodology estimate that \nwe have a SN Ia typing efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) \nSNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. \nWe quantify and correct for our selection effects (e.g., Malmquist bias) using simulations. When fitting \nto a flat _CDM cosmological model, we find that our photometric sample alone gives Ωm = 0.24+0.07/−0.05 \n(statistical errors only). If we relax the constraint on flatness, then our sample provides competitive \njoint statistical constraints on Ωm and Ω∆, comparable to those derived from the spectroscopically- \nconfirmed three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics–only \nresult favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological \nmodel, and combining with H0, CMB and LRG data, we obtain w = −0.96+0.10/−0.10, Ωm = 0.29+0.02/−0.02 and Ωk = 0.00+0.03/−0.02 (statistical errors only), which is competitive with similar spectroscopically confirmed \nSNe Ia analyses. Overall this comparison is reassuring, considering the lower redshift leverage of the \nSDSS-II SN sample (z < 0.55) and the lack of spectroscopic confirmation used herein. These results \ndemonstrate the potential of photometrically–classified SNe Ia samples in improving cosmological \nconstraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".