Prediction of the planet yield of the MaxProtoPlanetS high-contrast survey for H-alpha protoplanets with MagAO-X based on first light contrasts
Bibliographic record
Abstract
Our past GAPplanetS survey over the last 5 years with the MagAO visible AO system discovered the first examples of accreting protoplanets (by direct observation of H-alpha emission). Examples include LkCa15 b (Sallum et al. 2015) and PDS70 b (Wagner et al. 2018). In this paper we review the science performance of the newly (Dec. 2019) commissioned MagAO-X extreme AO system. In particular, we use the vAPP coronagraphic contrasts measured during MagAO-X first light. We use the Massive Accreting Gap (MAG) protoplanet model of Close 2020 to predict the H-alpha contrasts of 19 of the best transitional disk systems (ages 1-5 Myr) for the direct detection of H-alpha from accretion of hydrogen onto these protoplanets. The MAG protoplanet model applied to the observed first light MagAO-X contrasts predict a maximum yield of 46±7 planets from 19 stars (42 of these planets would be new discoveries). This suggests that there is a large, yet, unexplored reservoir of protoplanets that can be discovered with an extreme AO coronagraphic survey of 19 of the best transitional disk systems. Based on our first light contrasts we predict a healthy yield of protoplanets from our MaxProtoPlanetS survey of 19 transitional disks with MagAO-X.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".