Optical properties of metal-poor T dwarf candidates
Bibliographic record
Abstract
Context. Metal-poor brown dwarfs are poorly understood because they are extremely faint and rare. Only a few candidates have been identified as T-type subdwarfs in infrared surveys and their optical properties remain unconstrained. Aims. We aim to improve the knowledge of the optical properties of T subdwarf candidates to break the degeneracy between metallicity and temperature and to investigate their atmospheric properties. Methods. Deep z-band images of ten known T subdwarf candidates were collected with the 10.4-m Gran Telescopio Canarias. Low-resolution optical spectra for two of them were obtained with the same telescope. Photometric measurements of the z-band flux were performed for all the targets and they were combined with infrared photometry in J, H, K, W1, and W2 bands from the literature to obtain the colours. The spectra were compared with solar-metallicity T dwarf templates and with laboratory spectra. Results. We found that the targets segregate into three distinct groups in the W1 − W2 versus z − W1 colour-colour diagram. Group I objects are mixed with solar-metallicity T dwarfs. Group III objects have W1 − W2 colours similar to T dwarfs but very red z − W1 colours. Group II objects lie between Group I and III. The two targets for which we obtained spectra are located in Group I and their spectroscopic properties resemble normal T dwarfs but with water features that are deeper and have a shape akin to pure water. Conclusions. We conclude that the W1 − W2 versus z − W1 colour-colour diagram is excellent to break the metallicity-temperature degeneracy for objects cooler than L-type ones. A revision of the spectral classification of a T subdwarf might be needed in the future, according to the photometric and spectroscopic properties of WISE1810 and WISE0414 in Group III discussed in this work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".