Mapping the Skies of Ultracool Worlds: Detecting Storms and Spots with Extremely Large Telescopes
Bibliographic record
Abstract
Abstract Extremely large telescopes (ELTs) present an unparalleled opportunity to study the magnetism, atmospheric dynamics, and chemistry of very-low-mass (VLM) stars, brown dwarfs, and exoplanets. Instruments such as the Giant Magellan Telescope–Consortium Large Earth Finder (GMT/GCLEF), the Thirty Meter Telescope’s Multi-Objective Diffraction-limited High-Resolution Infrared Spectrograph (TMT/MODHIS), and the European Southern Observatory’s Mid-Infrared ELT Imager and Spectrograph (ELT/METIS) provide the spectral resolution and signal-to-noise ratio necessary to Doppler image ultracool targets’ surfaces based on temporal spectral variations due to surface inhomogeneities. Using our publicly available code, Imber, developed and validated in Plummer & Wang, we evaluate these instruments’ abilities to discern magnetic starspots and cloud systems on a VLM star (TRAPPIST-1), two L/T transition ultracool dwarfs (VHS J1256−1257 b and SIMP J0136+0933), and three exoplanets (Beta Pic b and HR 8799 d and e). We find that TMT/MODHIS and ELT/METIS are suitable for Doppler imaging the ultracool dwarfs and Beta Pic b over a single rotation. Uncertainties for longitude and radius are typically ≲10°, and latitude uncertainties range from ∼10° to 30°. TRAPPIST-1's edge-on inclination and low υ sin i provide a challenge for all three instruments, while GMT/GCLEF and the HR 8799 planets may require observations over multiple rotations. We compare the spectroscopic technique, photometry-only inference, and the combination of the two. We find combining spectroscopic and photometric observations can lead to improved Bayesian inference of surface inhomogeneities and offers insight into whether ultracool atmospheres are dominated by spotted or banded features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".