SpeX SPECTROSCOPY OF UNRESOLVED VERY LOW MASS BINARIES. II. IDENTIFICATION OF 14 CANDIDATE BINARIES WITH LATE-M/EARLY-L AND T DWARF COMPONENTS
Bibliographic record
Abstract
Multiplicity is a key statistic for understanding the formation of very low mass (VLM) stars and brown dwarfs. Currently, the separation distribution of VLM binaries remains poorly constrained at small separations (⩽1 AU), leading to uncertainty in the overall binary fraction. We approach this problem by searching for late-M/early-L plus T dwarf spectral binaries whose combined light spectra exhibit distinct peculiarities, allowing for separation-independent identification. We define a set of spectral indices designed to identify these systems, and we use a spectral template fitting method to confirm and characterize spectral binary candidates from a library of 815 spectra from the SpeX Prism Spectral Libraries. We present 11 new binary candidates, confirm 3 previously reported candidates, and rule out 2 previously identified candidates, all with primary and secondary spectral types in the range M7–L7 and T1–T8, respectively. We find that subdwarfs and blue L dwarfs are the primary contaminants in our sample and propose a method for segregating these sources. If confirmed by follow-up observations, these systems may add to the growing list of tight separation binaries, whose orbital properties may yield further insight into brown dwarf formation scenarios.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".