Correlative X‐Ray and Neutron Tomography of Microbial Biosignatures: Perspectives for Mars Sample Return

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Mars Sample Return (MSR) is the next major frontier in Mars exploration, through which advanced laboratory instrumentation will be used to study Martian samples and answer key questions surrounding Mars' planetary evolution and astrobiology. Non‐destructive, high‐resolution imaging will be critical for preliminary MSR analyses. Among the many functional analogs appropriate for biosignature detection training, Precambrian microbialites are considered uniquely representative of microbial ecosystems considered plausible on Noachian–Hesperian Mars. Here, we assessed the feasibility of interpreting coupled tomographic data sets using mature (∼1 Ga) biosignature‐bearing stromatolites from the Mbuji‐Mayi Supergroup (DR Congo). Samples were imaged using X‐ray μCT and neutron tomography (NT) to obtain high‐resolution 3D visualizations, which were evaluated for complementarity. By correlating tomographic data from both techniques, we provide spatially resolved 3D insights into compositional variations, including the presence of organic‐bearing morphological biosignatures, while resolving complex phase relationships. Although X‐ray μCT offers higher resolution insights into density variations and rock microstructure, our novel usage of NT to analyze stromatolites provides effective characterization of light element‐dominated phases. The accuracy of textural conclusions based on tomographic data are evaluated using optical microscopy and the compositional characteristics are verified using Scanning Electron Microscopy With Energy‐Dispersive X‐Ray Spectroscopy elemental mapping. Our results highlight the utility of correlated X‐ray and NT during the preliminary stages of MSR sample analysis and support the integration of these techniques into workflows in the MSR Sample Receiving Facility. Uniting data sets from highly complementary tomographic techniques addresses the fundamental aim of MSR analytical strategies: to maximize data output from highly limited MSR sample quantities.