Color classification of Earth-like planets with machine learning
Bibliographic record
Abstract
ABSTRACT Atmospheric characterization of directly imaged exoplanets is currently limited to Giant planets and Mini-Neptunes. However, upcoming ground-based Extremely Large Telescopes (ELTs) and space-based concepts such as Origins, HabEx, and LUVOIR are designed to characterize rocky exoplanets. But spectroscopy of Earth-like planets is time-intensive even for upcoming telescopes; therefore, initial photometry has been discussed as a promising avenue to faster classify and prioritize exoplanets. Thus, in this article we explore whether photometric flux – using the standard Johnson filters – can identify the existence of surface-life by analysing a grid of 318 780 reflection spectra of nominal terrestrial planets with 1 Earth radius, 1 Earth mass, and modern Earth atmospheres for varying surface compositions and cloud coverage. Because different kinds of biota change the reflection spectra, we assess the sensitivity of our results to six diverse biota samples including vegetation, representative of modern Earth, a biofilm as a way for microbes to survive extreme environments, and UV radiation resistant biota. We test the performance of several supervised machine-learning algorithms in classifying planets with biota for different signal-to-noise ratios: Machine-learning methods can detect the existence of biota using only the photometric flux of Earth-like planets’ reflected light with a balanced accuracy between 50 per cent and up to 75 per cent. These results assess the possibility that photometric flux could be used to initially identify biota on Earth-like planets and the trade-off between two critical results when classifying biota: false-positive and false-negative rates. Our spectra library is available online and can easily be used to test different filter combinations for upcoming missions and mission designs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".