Bibliographic record
Abstract
The Rapid Transient Surveyor (RTS) is a proposed rapid-response, high-cadence adaptive optics (AO) facility for the UH 2.2-m telescope on Maunakea. RTS will uniquely address the need for high-acuity and sensitive near-infrared spectral follow-up observations of tens of thousands of objects in mere months by combining an excellent observing site, unmatched robotic observational efficiency, and an AO system that significantly increases both sensitivity and spatial resolving power. We will initially use RTS to obtain the infrared spectra of ∼4,000 Type Ia supernovae identified by the Asteroid Terrestrial-Impact Last Alert System over a two year period that will be crucial to precisely measuring distances and mapping the distribution of dark matter in the z < 0.1 universe. RTS will comprise an upgraded version of the Robo-AO laser AO system and will respond quickly to target-of-opportunity events, minimizing the time between discovery and characterization. RTS will acquire simultaneous-multicolor images with an acuity of 0.07–0.10" across the entire visible spectrum (20% i′-band Strehl in median conditions) and <0.16" in the near infrared, and will detect companions at 0.5" at contrast ratio of ∼500. The system will include a high-efficiency prism integral field unit spectrograph: R = 70-140 over a total bandpass of 840–1830nm with an 8.7" by 6.0" field of view (0.15" spaxels). The AO correction boosts the infrared point-source sensitivity of the spectrograph against the sky background by a factor of seven for faint targets, giving the UH 2.2-m the H-band sensitivity of a 5.7-m telescope without AO.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.044 |
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".