Exoplanet instrumentation in the 2020s: Canada's pathway towards searching for life on potentially Earth-like exoplanets
Bibliographic record
Abstract
The next decade presents a unique moment in the history of planetary astronomy. For the first time, we have the technologies at hand to discover and characterize a wide range of exoplanetary systems, possibly harboring true Earth analogues. The opportunity is no less than answering humanity’s millennia old questions of “Are we alone?” and “How did we get here?”. The best part: Canada can play a leading role in this historic endeavor, if we make deliberate strategic investments over the next decade. In this white paper, we lay out pathways to develop the necessary instrumentation in collaboration with national and international partners to address the most fundamental questions regarding the formation of planets, the diversity of planetary systems, and the frequency of life in the universe. We recommend critical investments in a portfolio of assets including high-dispersion coronagraphy instrumentation for the upcoming ground based 30-meter class telescopes, a strong (JWST-scale) Canadian involvement in the next generation space missions LUVOIR or HabEx, and support for small and large space missions led by Canada. Importantly, while the science case of searching for biomarkers on rocky exoplanets presents the most stringent design requirements, the proposed instrumentation will also be ideal for the characterization of giant exoplanets, sub-Neptunes, and super-Earths and address a wide range of science questions in the coming decade.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".