The meaning of WISE colours – I. The Galaxy and its satellites
Bibliographic record
Abstract
Through matches with the Sloan Digital Sky Survey (SDSS) catalogue we identify the location of various families of astronomical objects in WISE colour space. We identify reliable indicators that separate Galactic/local from extragalactic sources and concentrate here on the objects in our Galaxy and its closest satellites. We develop colour and magnitude criteria that are based only on WISE data to select asymptotic giant branch (AGB) stars with circumstellar dust shells, and separate them into O-rich and C-rich classes. With these criteria we produce an all-sky map for the count ratio of the two populations. The map reveals differences between the Galactic disc, the Magellanic Clouds and the Sgr Dwarf Spheroidal galaxy, as well as a radial gradient in the Large Magellanic Cloud (LMC) disc. We find that the C:O number ratio for dusty AGB stars increases with distance from the LMC centre about twice as fast as measured for near-IR selected samples of early AGB stars. Detailed radiative transfer models show that WISE colours are well explained by the emission of centrally heated dusty shells where the dust has standard properties of interstellar medium (ISM) grains. The segregation of different classes of objects in WISE colour space arises from differences in properties of the dust shells: those around young stellar objects have uniform density distributions while in evolved stars they have steep radial profiles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".