Observing GRBs and Supernovae at Gemini Observatory as Target of Opportunity (ToO)
Bibliographic record
Abstract
Abstract The Gemini Observatories primarily operate a multi-instrument queue, with observers selecting observations that are best suited to weather and seeing conditions. The Target of Opportunity (ToO) observing mode is intended to allow observation of targets that cannot be specified in advance but which have a well defined external trigger such as distant supernovae or Gamma Ray bursts. In addition, the instrument and configuration best suited to observe the ToO may depend on properties of the event, such as brightness and redshift which again are impossible to know in advance. Queue observing naturally lends itself to Target of Opportunity (ToO) support since the time required to switch between programs and instruments is very short, and the staff observer is trained to operate all the available instruments and modes. Gemini Observatory has supported pre-approved ToO programs since beginning queue operations, and has implemented a rapid (less than 15 minutes response time) ToO mode since 2005. ToOs comprise a significant fraction of the queue (20–25% of the highest ranking band) nowadays. We discuss the ToO procedures, the statistics of rapid ToOs observing at Gemini North Observatory, the science related to GRBs and supernovae that this important mode has enabled.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".