Preprint typeset using L ATEX style emulateapj v. 6/22/04 GEMINI SPECTROSCOPY OF SUPERNOVAE FROM SNLS: IMPROVING HIGH REDSHIFT SN SELECTION AND CLASSIFICATION
Bibliographic record
Abstract
We present new techiques for improving the efficiency of supernova (SN) classification at high redshift using 64 candidates observed at Gemini North and South during the first year of the Supernova Legacy Survey (SNLS). The SNLS is an ongoing five year project with the goal of measuring the equation of state of Dark Energy by discovering and following over 700 high-redshift SNe Ia using data from the Canada-France-Hawaii Telescope Legacy Survey. We achieve an improvement in the SN Ia spectroscopic confirmation rate: at Gemini 71 % of candidates are now confirmed as SNe Ia, compared to 54 % using the methods of previous surveys. This is despite the comparatively high redshift of this sample, where the median SN Ia redshift is z = 0.81 (0.155 ≤ z ≤ 1.01). These improvements were realized because we use the unprecedented color coverage and lightcurve sampling of the SNLS to predict whether a candidate is an SN Ia and estimate its redshift, before obtaining a spectrum, using a new technique called the “SN photo-z. ” In addition, we have improved techniques for galaxy subtraction and SN template χ 2 fitting, allowing us to identify candidates even when they are only 15 % as bright as the host galaxy. The largest impediment to SN identification is found to be host galaxy contamination of the spectrum – when the SN was at least as bright as the underlying
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.746 | 0.711 |
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".