The Case for Probe-class NASA Astrophysics Missions
Bibliographic record
Abstract
Astrophysics spans an enormous range of questions on scales from individual planets to the entire cosmos. To address the richness of 21st century astrophysics requires a corresponding richness of telescopes spanning all bands and all messengers. Much scientific benefit comes from having the multi-wavelength capability available at the same time. Most of these bands,or measurement sensitivities, require space-based missions. Historically, NASA has addressed this need for breadth with a small number of flagship-class missions and a larger number of Explorer missions. While the Explorer program continues to flourish, there is a large gap between Explorers and strategic missions. A fortunate combination of new astrophysics technologies with new, high capacity, low dollar-per-kg to orbit launchers, and new satellite buses allow for cheaper missions with capabilities approaching strategic mission levels. NASA has recognized these developments by calling for Probe-class mission ideas for mission studies, spanning most of the electromagnetic spectrum from GeV gamma-rays to the far infrared, and the new messengers of neutrinos and ultra-high energy cosmic rays. The key insight from the Probes exercise is that order-of-magnitude advances in science performance metrics are possible across the board for initial total cost estimates in the range 500M-1B dollars.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".