La recherche de naines brunes et d'exoplanetes: developpement d'une technique d'imagerie multibande
Bibliographic record
Abstract
The technique of simultaneous spectral differential imaging (SSDI) is used to image directly brown dwarfs and exoplanets around nearby stars. An analytical PSF model is first presented with numerical simulations to estimate the PSF noise attenuation performance. A three-wavelength infrared camera (TRIDENT) implementing the SSDI technique is then described. Results from a survey of 35 nearby stars carried out with TRIDENT at the Canada-France-Hawaii telescope are presented. Performance estimates show that a companion 9.5 magnitudes fainter than a star is detectable (6 sigma) at 0.5" separation. An analysis of the observations suggests that non-common path aberrations between TRIDENT optical channels are the limiting factor preventing further PSF noise attenuation. A new camera concept using a multi-wavelength detector featuring a microlens array combined with micro-filters is presented to overcome the non-common path aberration problem. Another imaging technique, differential angular imaging, is also discussed to obtain a reference PSF in each optical channel while observing a target. Finally, the accuracy of FFT-based image shifting, scaling and rotating algorithms is studied to show that a companion 10^9 times fainter than a star can theoretically be detected by those algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".