Korea Microlensing Telescope Network: science cases
Bibliographic record
Abstract
We present the science cases with the Korea Microlensing Telescope Network (KMTNet) which consists of three widefield 1.6 m telescopes distributed in Chile, South Africa, and Australia, respectively, providing unique continuous sky coverage with the three telescopes. The primary scientific goal of the KMTNet project is to explore the structure and diversity of planetary systems and variable objects. Since the system is mainly optimized to conduct gravitational microlensing surveys, it will enable detections of very low-mass exoplanets, potentially down to the mass of Mars that are inaccessible by other ground-based techniques. In addition to the primary science, it is possible to conduct a variety of other observational programs with the KMTNet system, including photometric studies of nearby galaxies and galaxy clusters, discovery of supernovae and their follow-up observations, and observations of near-Earth objects. We expect synergies between the KMTNet project with other similar or complementary projects in the southern sky, such as SkyMapper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".