A novel water-band technique to identify brown dwarfs and planetary mass objects in the Solar neighbourhood
Bibliographic record
Abstract
In star-forming regions, the low-mass brown dwarfs and free-floating planets are vital tracers of the low mass end of star formation and are key analogues to exoplanets around stars. The complete census of a star-forming cloud, to masses well below the deuterium-burning limit, will also constrain the very low-mass end of the IMF. Our team surveys various nearby young star-forming regions in the solar neighborhood like Taurus, Serpens, IC348 etc. in search of brown dwarfs and planetary mass objects using a novel and robust technique which photometrically identifies these ultra cool objects. Here we present the study of one such nearby young star-forming region, Sigma Orionis located at a distance of ~400pc with very low extinction (Av<1mag). We use the near-IR WIRCam data from the 3.6m Canada-France Hawaii Telescope (CFHT) in the custom W-band filter (centred at 1.45µm water absorption feature) combined with the data from the J and H broadband filters to classify brown dwarfs based on a reddening insensitive index (Q). The Q-index distinguishes the sub-stellar objects below the hydrogen burning limit (i.e. M6 spectral type) and the sub-stellar objects thus identified were then spectroscopically followed-up using the SpeX spectrograph on the 3.2m NASA Infrared Telescope Facility (IRTF). All our candidate brown dwarfs are found to have spectral types M5-M8.5 and along with data from previous studies we have the complete census of spectroscopically confirmed members of the region up to ~15MJup. This shows that with our novel approach we can efficiently identify low-mass objects. Studying these brown dwarfs enables us to probe the IMF down to the sub-stellar regime and aid in understanding their formation mechanism in one of the well-known young clusters in the solar neighborhood for the first time. Our study explores the potential environmental influence on the brown dwarf formation scenario by performing a comparative analysis with the nearby well-studied star-forming regions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".