Analysis of Early Science observations with the CHaracterising ExOPlanets Satellite (<i>CHEOPS</i>) using<scp>pycheops</scp>
Bibliographic record
Abstract
ABSTRACT CHEOPS (CHaracterising ExOPlanet Satellite) is an ESA S-class mission that observes bright stars at high cadence from low-Earth orbit. The main aim of the mission is to characterize exoplanets that transit nearby stars using ultrahigh precision photometry. Here, we report the analysis of transits observed by CHEOPS during its Early Science observing programme for four well-known exoplanets: GJ 436 b, HD 106315 b, HD 97658 b, and GJ 1132 b. The analysis is done using pycheops, an open-source software package we have developed to easily and efficiently analyse CHEOPS light-curve data using state-of-the-art techniques that are fully described herein. We show that the precision of the transit parameters measured using CHEOPS is comparable to that from larger space telescopes such as Spitzer Space Telescope and Kepler. We use the updated planet parameters from our analysis to derive new constraints on the internal structure of these four exoplanets.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".