Science Autonomy for Ocean Worlds Astrobiology: A Perspective.

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of "features" (e.g., proposed biosignatures) and novelties (uncharacterized, "new" features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

Team Processes and Outcomes During the AMADEE-18 Mars Analog Mission.

The aim of this study was to examine team functioning within the context of the AMADEE 18 Mars analog project, which took place in Oman in the winter of 2018. Five "Analog Astronauts" participated in this study. Each completed measures of individual-level variables, including demographics and personality, before the simulated Mars mission began. At several time points during the mission, and once at the end, participants completed measures of individual stress reactions, and teamwork-related variables, including several types of team conflict, citizenship behavior, in-role behavior, counterproductive behavior, and social loafing. Each participant also reported how well he or she felt the team performed. The results indicate an overall positive, successful teamwork experience. Factors including measurement issues, psychological simulation fidelity, and qualities of the team likely influenced these results. Measuring important team- and individual-level variables during additional space analog events, while considering factors related to psychological fidelity, will allow for the compilation of data to better understand the factors affecting teams in these unusual contexts.

The subsurface geology of Río Tinto: material examined during a simulated Mars drilling mission for the Mars Astrobiology Research and Technology Experiment (MARTE).

The 2005 Mars Astrobiology Research and Technology Experiment (MARTE) project conducted a simulated 1-month Mars drilling mission in the Río Tinto district, Spain. Dry robotic drilling, core sampling, and biological and geological analytical technologies were collectively tested for the first time for potential use on Mars. Drilling and subsurface sampling and analytical technologies are being explored for Mars because the subsurface is the most likely place to find life on Mars. The objectives of this work are to describe drilling, sampling, and analytical procedures; present the geological analysis of core and borehole material; and examine lessons learned from the drilling simulation. Drilling occurred at an undisclosed location, causing the science team to rely only on mission data for geological and biological interpretations. Core and borehole imaging was used for micromorphological analysis of rock, targeting rock for biological analysis, and making decisions regarding the next day's drilling operations. Drilling reached 606 cm depth into poorly consolidated gossan that allowed only 35% of core recovery and contributed to borehole wall failure during drilling. Core material containing any indication of biology was sampled and analyzed in more detail for its confirmation. Despite the poorly consolidated nature of the subsurface gossan, dry drilling was able to retrieve useful core material for geological and biological analysis. Lessons learned from this drilling simulation can guide the development of dry drilling and subsurface geological and biological analytical technologies for future Mars drilling missions.

The 2005 MARTE Robotic Drilling Experiment in Río Tinto, Spain: objectives, approach, and results of a simulated mission to search for life in the Martian subsurface.

The Mars Astrobiology Research and Technology Experiment (MARTE) simulated a robotic drilling mission to search for subsurface life on Mars. The drill site was on Peña de Hierro near the headwaters of the Río Tinto river (southwest Spain), on a deposit that includes massive sulfides and their gossanized remains that resemble some iron and sulfur minerals found on Mars. The mission used a fluidless, 10-axis, autonomous coring drill mounted on a simulated lander. Cores were faced; then instruments collected color wide-angle context images, color microscopic images, visible-near infrared point spectra, and (lower resolution) visible-near infrared hyperspectral images. Cores were then stored for further processing or ejected. A borehole inspection system collected panoramic imaging and Raman spectra of borehole walls. Life detection was performed on full cores with an adenosine triphosphate luciferin-luciferase bioluminescence assay and on crushed core sections with SOLID2, an antibody array-based instrument. Two remotely located science teams analyzed the remote sensing data and chose subsample locations. In 30 days of operation, the drill penetrated to 6 m and collected 21 cores. Biosignatures were detected in 12 of 15 samples analyzed by SOLID2. Science teams correctly interpreted the nature of the deposits drilled as compared to the ground truth. This experiment shows that drilling to search for subsurface life on Mars is technically feasible and scientifically rewarding.