A Novel Survey for Young Substellar Objects with the W-band Filter. VI. Spectroscopic Census of Substellar Members and the IMF of the σ Orionis Cluster
Bibliographic record
Abstract
Abstract Low-mass stars and substellar objects are essential in tracing the initial mass function (IMF). We study the nearby young σ Orionis cluster (d ∼ 408 pc, age ∼ 1.8 Myr) using deep near-infrared (NIR) photometric data in the J, W, and H bands from WIRCam on the Canada–France–Hawaii Telescope. We use the water absorption feature to select brown dwarfs photometrically and confirm their nature spectroscopically with IRTF-SpeX. Additionally we select candidate low-mass stars for spectroscopy and analyze their membership and those of literature sources using astrometry from Gaia DR3. We obtain NIR spectra for 28 very-low-mass stars and brown dwarfs and estimate their spectral type between M3 and M8.5 (masses ranging between 0.3 and 0.01 M ⊙). Apart from these, we also identify five new planetary-mass candidates which require further spectroscopic confirmation of youth. We compile a comprehensive catalog of 170 spectroscopically confirmed members in the central region of the cluster, for a wide mass range of ∼19–0.004 M ☉. We estimate the star-to-brown-dwarf ratio to be ∼4, within the range reported for other nearby star-forming regions. With the updated catalog of members we trace the IMF down to 4 M Jup and we find that a two-segment power law fits the substellar IMF better than a log-normal distribution.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 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".