Precision astrometry with adaptive optics: constraints on the mutual orbit of Luhman 16AB from GeMS
Bibliographic record
Abstract
ELTs equipped with MCAO systems will be powerful astrometric tools in the next two decades. With sparse-field precisions exceeding 30 uas for V > 18, the ELTs will surpass even GAIA's per-epoch precision for faint stars (V > 12). We present results from an ongoing astrometry program with Gemini GeMS and discuss synergies with WFIRST and GAIA. First, we present a fit to the relative orbit of the individual L/T components of Luhman16 AB, the nearest brown dwarf binary known. Exploiting GeMS' wide field of view to image reference stars, we are able to track the relative motion to better than 0.2 mas. We find that a mutual Keplerian orbit with no perturbing planets fits the binary separation to within the measurement errors, ruling out companions down to 14 earth masses for certain orbits and periods.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".