Cloud Atlas: Hubble Space Telescope Near-infrared Spectral Library of Brown Dwarfs, Planetary-mass Companions, and Hot Jupiters
Bibliographic record
Abstract
Bayesian atmospheric retrieval tools can place constraints on the properties of brown dwarfs' and hot Jupiters' atmospheres. To fully exploit these methods, high signal-to-noise spectral libraries with well-understood uncertainties are essential. We present a high signal-to-noise spectral library (1.10-1.69 μm) of the thermal emission of 76 brown dwarfs and hot Jupiters. All our spectra have been acquired with the Hubble Space Telescope’s Wide Field Camera 3 instrument and its G141 grism. The near-infrared spectral types of these objects range from L4 to Y1. Eight of our targets have estimated masses below the deuterium-burning limit. We analyze the database to identify peculiar objects and/or multiple systems, concluding that this sample includes two very-low-surface-gravity objects and five intermediate-surface-gravity objects. In addition, spectral indices designed to search for composite-atmosphere brown dwarfs indicate that eight objects in our sample are strong candidates to have such atmospheres. None of these objects are overluminous, so their composite atmospheres are unlikely to be companion-induced artifacts. Five of the eight confirmed candidates have been reported as photometrically variable, suggesting that composite atmospheric indices are useful in identifying brown dwarfs with strongly heterogeneous cloud covers. We compare hot Jupiters and brown dwarfs in a near-infrared color-magnitude diagram. We confirm that the coldest hot Jupiters in our sample have spectra similar to mid-L dwarfs, and the hottest hot Jupiters have spectra similar to those of M-dwarfs. Our sample provides a uniform data set of a broad range of ultracool atmospheres, allowing large-scale comparative studies and providing an HST legacy spectral library.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.004 |
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".