Emergence and Sedimentation of Silicate Clouds in Substellar Atmospheres Using Spitzer IRS Spectra
Bibliographic record
Abstract
We reprocess and analyze all 113 mid-infrared low-resolution spectra of field M5-T9 dwarfs observed with the Spitzer Infrared Spectrograph (IRS), focusing on the main water, methane, ammonia, and silicate absorption bands. Silicate absorption at 8-11 micron is broadly detected in objects with spectral types between L2 and L8, and peaks over L4-L6. An analysis of the correlation between silicate absorption strength, near-infrared color excesses, and previously reported photometric variability provides the most comprehensive evidence so far that cloud condensates in L dwarfs are made of silicates and for the first time observationally establishes their emergence and sedimentation between effective temperatures of 2000 K and 1300 K. Results from this analysis further confirm that these silicate clouds are responsible of the observed color scatter and photometric variability in L dwarfs. About 60% of the IRS spectra in our study are presented for the first time, and the full set of uniformly reduced spectra is now made publicly available. The data can be used for further studies of substellar atmospheres, and as context for ultracool dwarf and exoplanet observations with the James Webb Space Telescope.
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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 | 0.001 | 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".