The CFHTLS Real Time Analysis System ”Optically Selected GRB Afterglows ” ⋆ I. Overview and performance
Bibliographic record
Abstract
Aims. We describe a wide and deep search for optical GRB afterglows on images taken with MegaCAM, the world largest astronomical CCD camera, at the Canada France Hawaii Telescope, within the framework of the CFHT Legacy Survey. Methods. This search is performed in near real-time thanks to a Real Time Analysis System (RTAS) called ”Optically Selected GRB Afterglows”, which has been completely realized and installed on a dedicated computer in Hawaii. This pipeline automatically and quickly analyzes Megacam images and extracts from them a list of astrometrically and photometrically variable objects which are displayed on a web page for validation by a member of the collaboration. Results. In this paper, we comprehensively describe the RTAS process. We also present statistical results based on nearly one full year of operation, showing the quality of the images and the performance of the RTAS. Finally, we compare the efficiency of this study with similar searches, propose an ideal observational strategy using simulations, and discuss general considerations on the searches for GRB afterglows independently of the prompt emission. Conclusions. This is the first of a series of two papers. A second paper will discuss the characterization of variable objects we have found, as well as the GRB afterglow candidates and their nature. We will also estimate or at least constrain the collimation factor of GRB using the
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".