Evidence for SiO cloud nucleation in the rogue planet PSO J318
Bibliographic record
Abstract
Silicate clouds have long been known to significantly impact the spectra of late L-type brown dwarfs – with observable absorption features at ~10 μm. The James Webb Space Telescope (JWST) has reopened a window to the mid-infrared with unprecedented sensitivity, bringing the characterization of silicate clouds into focus again. Using JWST, we aim to characterize the planetary-mass brown dwarf PSO J318.5338-22.8603, concentrating on any silicate cloud absorption the object may exhibit. PSO J318’s spectrum is extremely red, and its flux is variable, both of which are thought to be hallmarks of cloud absorption. We present JWST NIRSpec PRISM, G395H, and MIRI MRS observations of PSO J318 from 1 to 18 μm. We introduce a method based on PSO J318’s brightness temperature to generate a list of cloud species that are likely present in its atmosphere. We tested for the species’ presence with petitRADTRANS retrievals. Using retrievals and grids from various climate models, we derived bulk parameters from PSO J318’s spectra, which are mutually compatible. Our retrieval results point to a solar to a slightly super-solar atmospheric C/O, a slightly super-solar metallicity, and a 12 C/ 13 C below ISM values. The atmospheric gravity proves difficult to constrain for both retrievals and grid models. Retrievals describing the flux of PSO J318 by mixing two 1D models (“two-column models”) appear favored over single-column models; this is consistent with PSO J318’s variability. The JWST observations also reveal a pronounced absorption feature at 10 μm. This absorption is best reproduced by introducing a high-altitude cloud layer of small (<0.1 μm) amorphous SiO grains. The retrieved particle size and location of the cloud is consistent with SiO condensing as cloud seeding nuclei. High-altitude clouds comprised of small SiO particles have been suggested in previous studies. Therefore, the SiO nucleation we potentially observe in PSO J318 could be a more widespread phenomenon.
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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 | 0.001 | 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 | 0.000 |
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".