Low mass T Tauri and young brown dwarf candidates in the Chamaeleon II dark cloud found by DENIS
Bibliographic record
Abstract
We define a sample designed to select low-mass T Tauri stars and young brown dwarfs using DENIS data in the Chamaeleon II molecular cloud. We use a star count method to construct an extinction map of the Chamaeleon II cloud. We select our low-mass T Tauri star and young brown dwarf candidates by their strong infrared colour excess in the I-J/J-K_s colour-colour dereddened diagram. We retain only objects with colours I-J>2, and spatially distributed in groups around the cloud cores. This provides a sample of 70 stars of which 4 are previously known T Tauri stars. We have carefully checked the reliability of all these objects by visual inspection on the DENIS images. Thanks to the association of the optical I-band to the infra-red J and K_s bands in DENIS, we can apply this selection method to all star formation regions observed in the southern hemisphere. We also identify six DENIS sources with X-ray sources detected by ROSAT. Assuming that they are reliable low-mass candidates and using the evolutionary models for low-mass stars, we estimate the age of these sources between 1 Myr and < 10 Myr.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".