TRIDENT: An Infrared Differential Imaging Camera Optimized for the Detection of Methanated Substellar
Bibliographic record
Abstract
Received; accepted – 2 – A near-infrared camera in use at the Canada-France-Hawaii Telescope (CFHT) and at the 1.6-m telescope of the Observatoire du Mont-Mégantic is described. The camera is based on a Hawaii-1 1024×1024 HgCdTe array detector. Its main feature is to acquire three simultaneous images at three wavelengths across the methane absorption bandhead at 1.6 µm, enabling, in theory, an accurate subtraction of the stellar point spread function (PSF) and the detection of faint close methanated companions. The instrument has no coronagraph and features fast data acquisition, yielding high observing efficiency on bright stars. The performance of the instrument is described, and it is illustrated by laboratory tests and CFHT observations of the nearby stars GL526, υ And and χ And. TRIDENT can detect (6σ) a methanated companion with ∆H = 9.5 at 0.5 ′′ separation from the star in one hour of observing time. Non-common path aberrations and amplitude modulation differences between the three optical paths are likely to be the limiting factors preventing further PSF attenuation. Instrument rotation and reference star subtraction improve the detection limit by a factor of 2 and 4 respectively. A PSF noise attenuation model is presented to estimate the non-common path wavefront difference effect on PSF subtraction performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".