SYSTEMATIC UNCERTAINTIES ASSOCIATED WITH THE COSMOLOGICAL ANALYSIS OF THE FIRST PAN-STARRS1 TYPE Ia SUPERNOVA SAMPLE
Bibliographic record
Abstract
We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) \nsample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. \ndescribes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic \nuncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of \nobserved Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II \ncalibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the \nproper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We \nestimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of \nthe full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including \nsystematic uncertainties in our analysis of only SNe measurements, we find w =−1.120+0.360 −0.206(Stat)+0.269 −0.291(Sys). \nWith additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and \nH0 measurements, we find w = −1.166+0.072 −0.069 and Ωm = 0.280+0.013 −0.012 (statistical and systematic errors added in \nquadrature). The significance of the inconsistency with w = −1 depends on whether we use Planck or Wilkinson \nMicrowave Anisotropy Probe measurements of the CMB: wBAO+H0+SN+WMAP = −1.124+0.083 −0.065.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".