Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression
Bibliographic record
Abstract
ABSTRACT In this work, we outline a new method for estimating dayside effective temperatures of exoplanets and associated uncertainties using Gaussian process (GP) regression. By applying our method to simulated observations, we show that the GP method estimates uncertainty more robustly than other model-independent approaches. We find that unbiased estimates of effective temperatures can be made using as few as three broad-band measurements (white-light HST WFC3 and the two warm Spitzer IRAC channels), although we caution that estimates made using only IRAC can be significantly biased. We then apply our GP method to the 12 hot Jupiters in the literature whose secondary eclipse depths have been measured by WFC3 and IRAC channels 1 and 2: CoRoT-2 b; HAT-P-7 b; HD 189733 b; HD 209458 b; Kepler-13A b; TrES-3 b; WASP-4 b; WASP-12 b; WASP-18 b; WASP-33 b; WASP-43 b; and WASP-103 b. We present model-independent dayside effective temperatures for these planets, with uncertainty estimates that range from ±66 to ±136 K.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".