The Independent Discovery of Planet Candidates around Low-mass Stars and Astrophysical False Positives from the First Two <i>TESS</i> Sectors
Bibliographic record
Abstract
Continuous data releases throughout the Transiting Exoplanet Survey Satellite ( TESS ) primary mission will provide unique opportunities for the exoplanet community at large to contribute to maximizing TESS 's scientific return via the discovery and validation of transiting planets. This paper introduces our independent pipeline for the detection of periodic transit events along with the results of its inaugural application to the recently released 2 minute light curves of low-mass stars from the first two TESS sectors. The stellar parameters within our sample are refined using precise parallax measurements from the Gaia DR2, which reduce the number of low-mass stars in our sample relative to those listed in the TESS Input Catalog. In lieu of the follow-up observations required to confirm or refute the planetary nature of transit-like signals, a validation of transit-like events flagged by our pipeline is performed statistically. The resulting vetted catalog contains eight probable blended eclipsing binaries, eight known TOIs, plus seven new planet candidates (PCs) smaller than 4 Earth radii. This work demonstrates the ability of our pipeline to detect sub-Neptune-sized PCs, which to date represent some of the most attractive targets for future atmospheric characterization via transmission or thermal emission spectroscopy and for radial velocity efforts aimed at the completion of the TESS level one requirement to deliver 50 planets smaller than 4 Earth radii with measured masses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".