Characterizing the Cloud Decks of Luhman 16AB with Medium-resolution Spectroscopic Monitoring<sup>∗</sup>
Bibliographic record
Abstract
Abstract We present results from a two-night R ∼ 4000 0.9–2.5 μm spectroscopic monitoring campaign of Luhman 16AB (L7.5 + T0.5). We assess the variability amplitude as a function of pressure level in the atmosphere of Luhman 16B: the more variable of the two components. The amplitude decreases monotonically with decreasing pressure, indicating that the source of variability—most likely patchy clouds—lies in the lower atmosphere. An unexpected result is that the strength of the K i absorption is higher in the faint state of Luhman 16B and lower in the bright state. We conclude that either the abundance of K i increases when the clouds roll in, potentially because of additional K i in the cloud itself, or that the temperature–pressure profile changes. We reproduce the change in K i absorption strengths with combinations of spectral templates to represent the bright and the faint variability states. These are dominated by a warmer L8 or L9 component, with a smaller contribution from a cooler T1 or T2 component. The success of this approach argues that the mechanism responsible for brown dwarf variability is also behind the diverse spectral morphology across the L-to-T transition. We further suggest that the L9–T1 part of the sequence represents a narrow but random ordering of effective temperatures and cloud fractions, obscured by the monotonic progression in methane absorption strength.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".