Preprint typeset using LATEX style emulateapj v. 5/2/11 EXOPLANETS FROM THE ARCTIC: THE FIRST WIDE-FIELD SURVEY AT 80◦N
Bibliographic record
Abstract
Located within 10 degrees of the North Pole, northern Ellesmere Island offers continuous darkness in the winter months. This capability can greatly enhance the detection efficiency of planetary transit surveys and other time domain astronomy programs. We deployed two wide-field cameras at 80◦N, near Eureka, Nunavut, for a 152-hour observing campaign in February 2012. The 16-megapixel-camera systems were based on com-mercial f/1.2 lenses with 70mm and 42mm apertures, and they continuously imaged 504 and 1,295 square degrees respectively. In total, the cameras took over 44,000 images and produced better-than-1 % precision light curves for approximately 10,000 stars. We describe a new high-speed astrometric and photometric data reduction pipeline designed for the systems, test several methods for the precision flat-fielding of images from very-wide-angle cameras, and evaluate the cameras ’ image qualities. We achieved a scintillation-limited pho-tometric precision of 1-2 % in each 10s exposure. Binning the short exposures into 10-min chunks provided a photometric stability of 2-3 millimagnitudes, sufficient for the detection of transiting exoplanets around the bright stars targeted by our survey. We estimate that the cameras, when operated over the full arctic winter, will be capable of discovering several transiting exoplanets around bright (mV < 9.5) stars. 1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.490 | 0.377 |
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".