Development of a technique to identify μm-sized organic matter in asteroidal material: An approach using machine learning

Asteroidal materials contain organic matter (OM), which records a number of extraterrestrial environments and thus provides a record of Solar System processes. OM contain essential compounds for the origin of life. To understand the origin and evolution of OM, systematic identification and detailed observation using in-situ techniques is required. While both nm- and μm-sized OM were studied previously, only a small portion of a given sample surface was investigated in each study. Here, a novel workflow was developed and applied to identify and classify μm-sized OM on mm-sized asteroidal materials. The workflow involved image processing and machine learning, enabling a comprehensive and non-biased way of identifying, classifying, and measuring the properties of OM. We found that identifying OM is more accurate by classification with machine learning than by clustering. On the approach of classification with machine learning, five algorithms were tested. The random forest algorithm was selected as it scored the highest in 4 out of 5 accuracy parameters during evaluation. The workflow gave modal OM abundances that were consistent with those identified manually, demonstrating that the workflow can accurately identify 1-15 μm-sized OM. The size distribution of OM was modeled using the power-law distribution, giving slope α values that were consistent with fragmentation processes. The shape of the OM was quantified using circularity and solidity, giving a positive correlation and indicating these properties are closely related. Overall, the workflow enabled identification of many OM quickly and accurately and the obtainment of chemical and petrographic information. Such information can help the selection of OM for further in-situ techniques, and elucidate the origin and evolution of OM preserved in asteroidal materials.