Validation of TESS exoplanet candidates orbiting solar analogues in the all-sky PLATO input catalogue
Bibliographic record
Abstract
ABSTRACT The Transiting Exoplanet Survey Satellite (TESS) is focusing on relatively bright stars and has found thousands of planet candidates. However, mainly because of the low spatial resolution of its cameras (≈ 21 arcsec/pixel), TESS is expected to detect several false positives (FPs); hence, vetting needs to be done. Here, we present a follow-up program of TESS candidates orbiting solar-analogue stars that are in the all-sky PLATO input catalogue. Using Gaia photometry and astrometry we built an absolute colour–magnitude diagram and isolated solar-analogue candidates’ hosts. We performed a probabilistic validation of each candidate using the vespa software and produced a prioritized list of objects that have the highest probability of being genuine transiting planets. Following this procedure, we eliminated the majority of FPs and statistically vetted 23 candidates. For this remaining set, we performed a stellar neighbourhood analysis using Gaia Early Data Release 3 and centroid motion tests, greatly enhancing the on-target probability of 12 of them. We then used publicly available high-resolution imaging data to confirm their transit source and found five new, fully validated planets. For the remaining candidates, we propose on–off photometry to further refine the list of genuine candidates and prepare for the subsequent radial velocity follow-up.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".