The ESSENCE Supernova Survey: Survey Optimization, Observations, and Supernova Photometry
Bibliographic record
Abstract
We describe the implementation and optimization of the ESSENCE supernova survey, which we have undertaken to measure the dark energy equation‐of‐state parameter, . We present a method for optimizing the survey exposure times and cadence to maximize our sensitivity to w for a given fixed amount of telescope time. For our survey on the CTIO 4 m telescope, measuring the luminosity distances and redshifts for supernovae at modest redshifts ( ) is optimal for determining w. We describe the data analysis pipeline based on using reliable and robust image subtraction to find supernovae automatically and in nearly real time. Since making cosmological inferences with supernovae relies crucially on accurate measurement of their apparent brightnesses, we describe our efforts to establish a thorough calibration of the CTIO 4 m telescope’s natural photometric system. In its first four years, ESSENCE has discovered and spectroscopically confirmed 102 Type Ia supernovae, at redshifts from 0.10 to 0.78, identified through an impartial, effective methodology for spectroscopic classification and redshift determination. We present the resulting light curves for all of the Type Ia supernovae found by ESSENCE and used in our measurement of w, presented in a companion paper by Wood‐Vasey and coworkers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".