Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor

Abstract

The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications. Icy moons in our solar system are promising targets for exploration because their liquid oceans make them potential habitats for microscopic life. However, the lack of a precise definition of life poses a fundamental challenge to formulating detection strategies. To increase the chances of unambiguous detection, a suite of complementary instruments must sample multiple independent biosignatures (e.g., composition, motility/behavior, and visible structure). Such an instrument suite could generate 10,000× more raw data than is possible to transmit from distant ocean worlds like Enceladus or Europa. To address this bandwidth limitation, Onboard Science Instrument Autonomy (OSIA) is an emerging discipline of flight systems capable of evaluating, summarizing, and prioritizing observational instrument data to maximize science return. We describe two OSIA implementations developed as part of the Ocean World Life Surveyor (OWLS) prototype instrument suite at the Jet Propulsion Laboratory. The first identifies life-like motion in digital holographic microscopy videos, and the second identifies cellular structure and composition via innate and dye-induced fluorescence. Flight-like requirements and computational constraints were used to lower barriers to infusion, similar to those available on the Mars helicopter, “Ingenuity.” We evaluated the OSIA's performance using simulated and laboratory data and conducted a live field test at the hypersaline Mono Lake planetary analog site. Our study demonstrates the potential of OSIA for enabling biosignature detection and provides insights and lessons learned for future mission concepts aimed at exploring the outer solar system.

Motion history images: a new method for tracking microswimmers in 3D

“…The advances in microscopy that have enabled these studies have made it possible to collect large amounts of data, but processing of the data remains difficult and time consuming, particularly for bacteria near the resolution limit of the instruments. For some applications, such as remote field instruments and planetary space missions, some degree of autonomous tracking is desirable in order to reduce the amount of data that needs to be sent over limited data links (Wronkiewicz and Lee, 2021;Wronkiewicz et al, 2024). We present here an approach designed to be as independent of prior sample knowledge as possible, while also being computationally lowcost.…”

“…To obtain the tracks, the holograms were reconstructed into Z stacks, then a maximum projection was taken through the Z stack to yield a higher signal-to-noise X,Y time series. These were then tracked using an open source software package developed by our team called Holographic Examination for Life-like Motility (HELM) (Wronkiewicz and Lee, 2021;Wronkiewicz et al, 2024). HELM offers color-coded Motion History Images (MHI) in addition to automated tracking based on the LAP algorithm.…”