Using machine learning to optimize the search for biosignatures

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 h...

Full description

Saved in:

Bibliographic Details
Published in:	Nature astronomy Vol. 7; no. 4; pp. 378 - 379
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 01-04-2023 Nature Publishing Group
Subjects:	639/33 639/33/445 Astrobiology Astronomy Astrophysics and Cosmology Deep learning Earth Geology Habitats Landscape ecology Machine learning Mars Physics Physics and Astronomy Probability Research Briefing Spectrum analysis
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
ISSN:	2397-3366 2397-3366
DOI:	10.1038/s41550-023-01894-1