Atmospheric characterization of hot Jupiters using hierarchical models of <i>Spitzer</i> observations
Bibliographic record
Abstract
ABSTRACT The field of exoplanet atmospheric characterization is trending towards comparative studies involving many planetary systems, and using Bayesian hierarchical modelling is a natural next step. Here we demonstrate two use cases. We first use hierarchical modelling to quantify variability in repeated observations by reanalysing a suite of 10 Spitzer secondary eclipse observations of the hot Jupiter XO-3 b. We compare three models: one where we fit 10 separate eclipse depths, one where we use a single eclipse depth for all 10 observations, and a hierarchical model. By comparing the widely applicable information criterion of each model, we show that the hierarchical model is preferred over the others. The hierarchical model yields less scatter across the suite of eclipse depths – and higher precision on the individual eclipse depths – than does fitting the observations separately. We find that the hierarchical eclipse depth uncertainty is larger than the uncertainties on the individual eclipse depths, which suggests either slight astrophysical variability or that single eclipse observations underestimate the true eclipse depth uncertainty. Finally, we fit a suite of published dayside brightness measurements for 37 planets using a hierarchical model of brightness temperature versus irradiation temperature. The hierarchical model gives tighter constraints on the individual brightness temperatures than the non-hierarchical model. Although we tested hierarchical modelling on Spitzer eclipse data of hot Jupiters, it is applicable to observations of smaller planets like hot Neptunes and super-Earths, as well as for photometric and spectroscopic transit or phase-curve observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".