The rapid-response, fully-automated, near-Earth asteroid follow-up program with the SAAO’s 1-m Lesedi telescope
Bibliographic record
Abstract
We introduce here a program that utilises the robotic observing capabilities of the South African Astronomical Observatory’s 1-meter Lesedi telescope, equipped with the Mookodi instrument to observe newly discovered near-Earth asteroids (NEAs). Observations are automatically scheduled and robotically observed on Lesedi using scripts that continuously monitor new discoveries being reported by the community to the The International Astronomical Union’s Minor Planet Center (IAU MPC), enabling rapid follow-up of newly discovered NEAs, the majority within hours of discovery. The main goal of the program is to study the understudied smaller NEA population, and for that reason this rapid response is essential, as smaller asteroids ( < 100 metres) quickly dim as they move away from Earth, making precise measurements with small-to-medium aperture telescopes challenging. Since the start of this project in February 2023, over 200 NEAs have been observed under this program with approximately 75% of all our observed asteroids having a diameter of less than 100 metres, thereby achieving the primary objective of the program. Although the program primarily targets smaller asteroids, it has also resulted in the observation of 15 NEAs that have subsequently been classified as potentially hazardous asteroids (PHAs). The astrometric data collected during this program have contributed to Minor Planet Electronic Circulars (MPECs) for ∼ 68% of observed targets, thereby contributing to orbital refinement and supporting the official designation of these NEAs — ultimately contributing to planetary defense efforts. • Demonstrates rapid-response follow-up of small near-Earth asteroids using the 1-m Lesedi telescope in South Africa. • Robotic system triggers observations within hours of discovery via NEOCP alerts. • Contributes astrometric data to the Minor Planet Center.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".