Towards a second-generation robotic telescope mount for the air-LUSI instrument
Bibliographic record
Abstract
Earth observation satellites, such as those responsible for monitoring the effects of climate change, require rigorous calibration protocols to account for on-orbit sensor degradation. An increasingly dependable method to address this issue uses the Moon as a reference light source for in-situ calibration. The airborne lunar spectral irradiance (air-LUSI) mission aims to improve the utility of the Moon as an on-orbit calibration target for remote sensing instruments, by tying the currently accepted lunar model to the SI and establishing lunar irradiance on an absolute scale. To this end, air-LUSI collects SI-traceable measurements of lunar irradiance at visible to nearinfrared wavelengths with unprecedented accuracy. A non-imaging telescope is flown at an altitude of 21 km, aboard NASA’s high-altitude ER-2 aircraft, which places the instrument above 95% of the Earth’s atmosphere for clean, minimally obstructed lunar spectra. To fix the optical axis on the Moon during flight, an autonomous control system is required to compensate for aircraft motion and track the Moon across its celestial transit. In this paper, we present an overview of the robotic subsystem used to track the Moon on more than ten high-altitude flights, and the valuable lessons learned from those campaigns. From this insight, a preliminary design for a second-generation robotic telescope mount is presented. Referred to as the HAAMR, it will supplant the current robotics system on future air-LUSI Operational Flight Campaigns, with the nearest field deployment slated for January 2024. We show how this new system is poised to offer a more reliable, accurate, and responsive platform for the air-LUSI instrument to continue collecting data that will ultimately help to improve our understanding of the Earth’s climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".