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Completed TRL 1 (started at 1, targeting 1)
Identification of cells, colonies, and biofabrics at any scale, whether in hand sample (mm scale) or in a microscope (e.g, sub-micron), is a skill learned through years of extensive practice and generally a significant amount mentorship. Such finely honed skills allow the expert to differentiate organisms of interest from other inorganic materials that may have similar shapes and textures, but which are inorganic. The creation of a well-trained algorithm as a pattern analysis package to a flight-capable imaging instrument would allow quick identification and assessment of rocks and other surfaces that warrant further chemical analysis – an ability that could speed the cadence of investigation for flight instruments and will be necessary on life detection missions where close human supervision for every analysis is impractical.
The proposed pilot study expands on current efforts to catalog biologic structures, textures, and fabrics on surfaces and in geologic substrates by training machine learning algorithms to identify these unique biosignatures from nano-scale rock topologies without the aid of a human microscopist/biologist.
This is a non-traditional optical method of biosignature detection that compliments existing techniques for discovering life evidence on other worlds. This method can be used to dramatically reduce the time and resources required when determining high-value specimens for analytical interrogation and/or sample return.
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