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Using machine learning to optimize the search for biosignatures

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A probabilistic machine learning-based framework for recognizing and predicting microbial landscape patterns at nested spatial scales was developed. The approach substantially increased the probability of detecting biosignatures when tested at a Martian analogue in the high Andes. This search tool has applications for detecting biosignatures on terrestrial or icy planets.

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Fig. 1: Biosignature probability maps from CNN models and statistical ecology data.

References

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This is a summary of: Warren-Rhodes, K. et al. Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues. Nat. Astron. https://doi.org/10.1038/s41550-022-01882-x (2023).

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Using machine learning to optimize the search for biosignatures. Nat Astron 7, 378–379 (2023). https://doi.org/10.1038/s41550-023-01894-1

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