WEIRD: Wide-orbit Exoplanet Search with InfraRed Direct Imaging
Bibliographic record
Abstract
Abstract We report results from the Wide-orbit Exoplanet search with InfraRed Direct imaging, or WEIRD, a survey designed to search for Jupiter-like companions on very wide orbits (1000–5000 au) around young stars (<120 Myr) that are known members of moving groups in the solar neighborhood (<70 pc). Companions that share the same age, distance, and metallicity as their host while being on large enough orbits to be studied as “isolated” objects make prime targets for spectroscopic observations, and they are valuable benchmark objects for exoplanet atmosphere models. The search strategy is based on deep imaging in multiple bands across the near-infrared domain. For all 177 objects of our sample, , J, [3.6], and [4.5] images were obtained with CFHT/MegaCam, GEMINI/GMOS, CFHT/WIRCam, GEMINI/Flamingos-2, and Spitzer/IRAC. Using this set of four images per target, we searched for sources with red and [3.6]–[4.5] colors, typically reaching good completeness down to 2 companions, while going down to 1 for some targets, at separations of 1000–5000 au. The search yielded four candidate companions with the expected colors, but they were all rejected through follow-up proper motion observations. Our results constrain the occurrence of 1–13 planetary-mass companions on orbits with a semimajor axis between 1000 and 5000 au at less than 0.03, with a 95% confidence level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".