^1,2E.Clave et al. (>10)
Journal of Geophysical Research: Planets (in Press) Open Access Link to Article [https://doi.org/10.1029/2025JE009107]
¹Deutsches Zentrum für Luft‐ und Raumfahrt e.V. (DLR), Institute of Space Research, Berlin, Germany
²UniversitéClaude Bernard Lyon 1, ENS de Lyon, CNRS, UJM, LGL‐TPE, UMR 5276, Villeurbanne cedex, France
Published by arrangement with John Wiley & Sons

Over 3.5 years of exploration in Jezero Crater, the Perseverance rover has explored several geological units of diverse origins and natures, performing multi-technique remote analyses of the chemistry and mineralogy of rocks with the SuperCam instrument suite. Three of these units are dominated by mafic to ultramafic rocks: the igneous rocks of Séítah (olivine cumulate on the crater floor), the sedimentary rocks of the Upper Fan (Western delta) and the Margin Unit, of likely igneous origin. Despite their diverse natures, these three different units present similar mineralogical assemblages with: (a) primary igneous minerals (olivine, pyroxene, Cr-rich Ti-Fe oxides), (b) Fe-Mg carbonate, (c) hydrated/hydroxylated silica, and (d) phyllosilicates. The abundance of carbonate is variable, and we estimate it around 3–9 wt.% and up to 6–16 wt.% carbonate mineral in the Upper Fan and Margin Unit, respectively. We propose that most of these carbonates formed through in situ carbonation of mafic/ultramafic material, whether these rocks were emplaced through igneous or sedimentary processes. The distribution of carbonate with elevation in the Upper Fan and Margin Unit suggests a contribution of the lacustrine activity to the carbonate process, possibly enhanced by hydrothermal activity. These in situ observations may be extrapolated to other carbonates-bearing rocks on Mars and would make the amount of carbon potentially stored in Martian ultramafic rocks overall significant. This would suggest that carbonation of ultramafic rocks might have played a key role in pumping CO2 from and therefore in cooling the Martian atmosphere.

^1,2Samantha Hemmelgarn, ²Nicholas Moskovitz, ^3,4Denis Vida
Icarus (in Press) Open Access Link to Article [https://doi.org/10.1016/j.icarus.2026.117128]
¹Department of Astronomy and Planetary Science, Northern Arizona University, 527 S Beaver St, Flagstaff, Flagstaff, AZ, 86011, USA
²Lowell Observatory, 1400 West Mars Hill Road, Flagstaff, AZ, 86001, USA
³Department of Physics and Astronomy, The University of Western Ontario, London, Ontario, Canada
⁴Institute for Earth and Space Exploration, The University of Western Ontario, London, Ontario, Canada
Copyright Elsevier

We use machine learning to develop a framework for classifying meteoroids based on 13 directly observed parameters from the Global Meteor Network. This method adds depth to the  parameter, which uses only three parameters. We employ a semi-qualitative approach using 28,177 meteor events observed in 2023 by the Lowell Observatory Cameras for All-Sky Meteor Surveillance (LO-CAMS) network to evaluate multiple normalization, dimensionality-reduction, and clustering algorithms. We find that a combination of Factor Analysis (FA) and a Gaussian Mixture Model (GMM) results in clusters most consistent with traditional models. Three FA-derived factors corresponding to meteoroid kinematics, activation thresholds, and size/geometry effects describe the underlying structure of meteoroid behavior. The activation factor emerged as the most discriminating factor distinguishing whether a meteor is of asteroidal or cometary origin. Resulting 3, 6, and 11 cluster models reveal progressively finer compositional structure, from broad physical regimes to detailed subdivisions within cometary and asteroidal populations. From these results, we introduce a physically motivated hardness classification scheme: .  is a data-driven extension of  which physically interprets clusters in terms of the densest iron meteoroids down to the softest cometary material. Application to nine well-studied meteor showers and analysis of clusters in orbital space aids in the physical interpretation of  groups. The  model is supported by an analytical FA–GMM formulation that enables application to future datasets. Our results demonstrate that machine learning methods can extract compositional information from modern optical meteor datasets at scale and offers a new framework for interpreting meteoroid populations.