hide
Free keywords:
machine learning; motility; biosignature; automation; species identification; life
detection
Abstract:
(1) Background: Future missions to potentially habitable places in the Solar System require
biochemistry‐independent methods for detecting potential alien life forms. The technology was not
advanced enough for onboard machine analysis of microscopic observations to be performed in past
missions, but recent increases in computational power make the use of automated in‐situ analyses
feasible. (2) Methods: Here, we present a semi‐automated experimental setup, capable of
distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior
of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and
Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these
species based on their characteristic motility behavior. (3) Results: While we were able to distinguish
microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding
99%, the accuracy of the automated identification rates for the selected species does not exceed 82%.
(4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming lifedetection
missions. This study serves as the basis for the further development of a microscopic life
recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.
