Hyperspectral datacube estimations of binary stars with the Computed Tomographic Imaging Spectrometer (CTIS)
Bibliographic record
Abstract
Using mathematical techniques recently adapted for the analysis of hyperspectral imaging systems such as the CTIS, we have performed datacube reconstructions for a number of binary star systems. The CTIS images in the visible (420nm to 720nm) wavelength range were obtained in 2001 using the 3.67m Advanced Electro Optical System (AEOS) of the Maui Space Surveillance System (MSSS). These methods used an analytical model of the CTIS to construct an imaging system operator from optical, focal plane array and Computer Generated Holographic (CGH) disperser parameters in the CTIS. We used the adjoint of this operator to construct matched filtered estimates of the datacubes from the image data. In these reconstructions we are able to simultaneously obtain information on the geometry and relative photometry of the binary systems as well as the spectrum for each component of the system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".