The<i>MOST</i>Asteroseismology Mission: Ultraprecise Photometry from Space
Bibliographic record
Abstract
The Microvariablity and Oscillations of Stars ( MOST ) mission is a low‐cost microsatellite designed to detect low‐degree acoustic oscillations (periods of minutes) with micromagnitude precision in solar‐type stars and metal‐poor subdwarfs. There are also plans to detect light reflected from giant, short‐period, extrasolar planets and the oscillations of roAp stars and the turbulent variability in the dense winds of Wolf‐Rayet stars. This paper describes the experiment and how we met the challenge of ultraprecise photometry despite severe constraints on the mass, volume, and power available for the instrument. A side‐viewing, 150 mm aperture Rumak‐Maksutov telescope feeds two frame‐transfer CCDs, one for tracking and the other for science. There is a single 300 nm wide filter centered at 525 nm. Microlenses project Fabry images of the brighter (V≤10) target stars onto the science CCD. Fainter target stars will be focused directly elsewhere on the CCD. MOST was launched on 2003 June 30 into a low‐Earth, Sun‐synchronous, polar orbit allowing stars between −19° and +36° declination to be viewed continuously for up to 60 days. Attitude is controlled by reaction wheels and magnetotorquers. A solar safety shutter over the telescope diagonal is the only other moving part. Accumulated photometry will be used to calibrate response across the target field stop, and data will be compressed and downloaded to three dedicated ground stations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.033 |
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".