THE<i>HUBBLE SPACE TELESCOPE</i>CLUSTER SUPERNOVA SURVEY. V. IMPROVING THE DARK-ENERGY CONSTRAINTS ABOVE<i>z</i>> 1 AND BUILDING AN EARLY-TYPE-HOSTED SUPERNOVA SAMPLE
Bibliographic record
Abstract
We present Advanced Camera for Surveys, NICMOS, and Keck adaptive-optics-assisted photometry of 20 Type Ia supernovae (SNe Ia) from the Hubble Space Telescope ( HST ) Cluster Supernova Survey. The SNe Ia were discovered over the redshift interval 0.623 < z < 1.415. Of these SNe Ia, 14 pass our strict selection cuts and are used in combination with the world's sample of SNe Ia to derive the best current constraints on dark energy. Of our new SNe Ia, 10 are beyond redshift z = 1, thereby nearly doubling the statistical weight of HST -discovered SNe Ia beyond this redshift. Our detailed analysis corrects for the recently identified correlation between SN Ia luminosity and host galaxy mass and corrects the NICMOS zero point at the count rates appropriate for very distant SNe Ia. Adding these SNe improves the best combined constraint on dark-energy density, ρ DE ( z ), at redshifts 1.0 < z < 1.6 by 18% (including systematic errors). For a flat ΛCDM universe, we find Ω Λ = 0.729 ± 0.014 (68% confidence level (CL) including systematic errors). For a flat w CDM model, we measure a constant dark-energy equation-of-state parameter w = −1.013 +0.068 −0.073 (68% CL). Curvature is constrained to ∼0.7% in the ow CDM model and to ∼2% in a model in which dark energy is allowed to vary with parameters w 0 and w a . Further tightening the constraints on the time evolution of dark energy will require several improvements, including high-quality multi-passband photometry of a sample of several dozen z > 1 SNe Ia. We describe how such a sample could be efficiently obtained by targeting cluster fields with WFC3 on board HST . The updated supernova Union2.1 compilation of 580 SNe is available at http://supernova.lbl.gov/Union .
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".