Recent Advances in the Pan-STARRS Search for Near-Earth Objects
Bibliographic record
Abstract
The Pan-STARRS telescopes on Haleakala, Maui, search the sky every night for objects that pass close to Earth. The Pan-STARRS1 telescope has been in operation for over 10 years. The Pan-STARRS2 telescope is now delivering similar performance, and together these telescopes can survey most of the available sky twice during each lunation. Pan-STARRS2 experienced some early technical issues. These included secondary mirror mount problems, and use of a silver coating on the secondary mirror, which degraded rapidly after Kilauea erupted on the island of Hawaii. The secondary mirror was recoated with protected aluminum, and the mirror mount problems solved. Pan-STARRS2 now delivers slightly better throughput compared to Pan-STARRS1, due to its newer mirror coating. Image quality is currently similar on each of the two telescopes, and is mostly limited by local atmospheric conditions (“seeing”). Prior to 2021, the reporting latency of Pan-STARRS did not allow for immediate follow up of new discoveries. But this latency has been reduced and Pan-STARRS now attempts same-night self-recovery of many of its newly discovered Near-Earth Objects. This extends the orbital arc from approximately 1 hour to 3–4 hours. Because of Earth rotation, the motion of the observer produces parallax that helps to constrain the distance and size of the Near-Earth Object, thereby reducing the future positional uncertainty, and aiding in recovery. One of the most important discoveries of Pan-STARRS to date is the interstellar object 'Oumuamua. It is likely that Pan-STARRS detected other interstellar objects in the past, but without rapid follow-up they were not recovered. The same-night follow up should aid in the discovery of interstellar objects in the future. Pan-STARRS produces excellent astrometry, and often sees curvature in the path of nearby Near-Earth Objects. This is caused by Earth's rotation, and is an indication that an object is very nearby. Pan-STARRS has not yet discovered an impacting asteroid before impact. However, the ability to see curvature, to-gether with the new capability for same-night follow up, and the improved sky coverage coming from two telescopes, makes Pan-STARRS a powerful tool for discovering impacting asteroids.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".