Bibliographic record
Abstract
Context. Keplerian-Stacker is an algorithm capable of combining multiple observations acquired at different epochs by taking into account the orbital motion of a potential planet present in the images to boost the ultimate detection limit. In 2019, a total of 100 h of observation was allocated to Very Large Telescope (VLT) Spectrometer and Imager for the mid-infrared (VISIR) instrument for the New Earths in the α Centauri Region (NEAR) survey, a collaboration between European Southern Observatory (ESO) and Breakthrough Initiatives, to search for low mass planets in the habitable zone of the α Cen AB binary system. A weak signal (S/N ~ 3) was reported around α Cen A, at a separation of ≃ 1.1 au, corresponding to the habitable zone. Aims. Our study is aimed at determining whether K-Stacker is also capable of detecting the low-mass planet candidate with similar orbital parameters, which was previously found by the NEAR team. We also aim to search for additional potential candidates around a Cen A by utilizing the orbital motion to boost the signal and by generally placing stronger constraints on the presence of other planets in the system. Methods. We re-analysed the NEAR data using K-Stacker. This algorithm is a brute-force method that is equipped to find planets in observational time series and to constrain their orbital parameters, even if they have remained undetected in a single epoch. Results. We scanned a total of about 3.5 × 105 independent orbits, among which close to 15% correspond to fast-moving orbits on which planets cannot be detected without taking into account the orbital motion. We found only a single planet candidate that matches the C1 detection reported in Wagner et al. (2021, Nat. Commun., 12, 922). However, since this constitutes a re-analysis of the same data set, more observations will be necessary to confirm that C1 is indeed a planet and not a disk or other data artifact. Despite the significant amount of time spent on this target, the orbit of this candidate remains poorly constrained due to these observations being closely distributed across 34 days. We argue that future single-target deep surveys would benefit from a K-Stacker based strategy, where the observations would be split over a significant part of the expected orbital period to better constrain the orbital parameters. Conclusions. This application of K-Stacker to high-contrast imaging data in the mid-infrared demonstrates the capability of this algorithm in aiding the search for Earth-like planets in the habitable zone of the nearest stars with future instruments of the E-ELT, such as METIS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.056 |
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".