Field studies at terrestrial analogue sites represent an important contribution to the science of ocean worlds. The value of the science and technology investigations conducted at field analogue sites depends on the relevance of the analogue environment to the target ocean world. We accept that there are no perfect analogues for many of the unique environments represented by ocean worlds but suggest that a one-to-one matching of environmental characteristics and conditions is not crucial to the success or impact of the work. Instead, we must instead determine which processes and parameters are required to map directly to the target ocean world environment with high fidelity to address the science question or engineering challenge. Where there are discrepancies between the model and target environment, we must fully understand how those limitations impact the applicability of the study, and mitigate these where possible using alternative approaches. Here we present a two-step approach to 1) identify the most crucial processes and parameters associated with a given science question and 2) assess the fidelity of these processes and parameters at a proposed field site to those expected for the target ocean world. We demonstrate this approach in a test case evaluating three types of ocean world analogue environments with respect to a science question. Our proposed framework will not only enhance the scientific rigor of field research but also provide access to a broader range of field sites relevant to ocean worlds processes, enabling a greater diversity of ocean and geological science researchers.

Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean worlds mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited compute resources. In response, the Autonomous Capillary Electrophoresis Mass-spectra Examination (ACME) onboard science autonomy system was created for application to the Ocean Worlds Life Surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, the uniqueness of an observation relative to previous observations, and the presence of key target compound signatures.

The Ocean Worlds Life Surveyor (OWLS) is a field prototype instrument suite designed to autonomously search for evidence of water-based life, developed in preparation for potential future missions to ocean worlds such as Enceladus and Europa. One instrument included in this suite is a Capillary Electrophoresis-Electrospray Ionization Mass Spectrometer (CE-ESI MS), which can detect the presence of organic molecules and other potential biosignature compounds. Due to the extreme energy costs involved in communication from these distant worlds, a mission’s downlink bandwidth is insufficient to return raw data from even a single recorded dataset. We developed two onboard capabilities to address this constraint: compression via knowledge summarization, and prioritization for the most scientifically useful observations. To summarize and prioritize the data generated by the CE-ESI MS, we developed the Autonomous CE-ESI Mass-Spectra Examination (ACME) system. ACME performs content summarization while ensuring that scientifically valuable signals are retained. First, ACME identifies and characterizes potential peaks in the mass spectra, each of which may indicate the presence of a specific compound. Then, ACME uses a decision tree model trained on expert-labeled data and peak properties such as width and signal-to-noise ratio to filter only for peaks of likely scientific interest. Finally, ACME produces a series of Autonomous Science Data Products (ASDPs): crops of small regions of the raw mass spectra data around each peak, a summary of the background noise to provide context and justification for its decisions, estimates of the scientific utility of the observation, and a brief description of its contents to enable downlink prioritization based on known science targets of interest as well as diversity sampling. Typical data sizes of the peak locations, crops, and background noise summary satisfy the mission downlink bandwidth constraints with an average compression ratio of 900:1. ACME was validated on lab- and field-collected data to confirm that scientists are able to successfully analyze and make valid scientific conclusions using only ACME’s ASDPs, compared to analyzing the raw data directly.