Development and status of MAPS, the MMT AO exoPlanet characterization system
Bibliographic record
Abstract
We are upgrading and refurbishing the first-generation adaptive-secondary mirror (ASM)-based AO system on the 6.5-m MMT in Arizona, in an NSF MSIP-funded program that will create a unique facility specialized for exoplanet characterization. This update includes a third-generation ASM with embedded electronics for low power consumption, two pyramid wavefront sensors (optical and near-IR), and an upgraded ARIES science camera for high-resolution spectroscopy (HRS) from 1-5 μm and MMT-POL science camera for sensitive polarization mapping. Digital electronics have been incorporated into each of the 336 actuators, simplifying hub-level electronics and reducing the total power to 300 W, down from 1800 W in the legacy system — reducing cooling requirements from active coolant to passive ambient cooling. An improved internal control law allows for electronic damping and a faster response. The dual pyramid wavefront sensors allow for a choice between optical or IR wavefront sensing depending on guide star magnitude, color, and extinction. The HRS upgrade to ARIES enables crosscorrelation of molecular templates to extract atmospheric parameters of exoplanets. The combination of these upgrades creates a workhorse instrument for exoplanet characterization via AO and HRS to separate planets from their host stars, with broad wavelength coverage and polarization to probe a range of molecular species in exoplanet atmospheres.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".