¹J. F. Pernet-Fisher,¹K. H. Joy,¹M. E. Hartley
Journal of Geophysical Research (Planets)(in Press) Link to Article [https://doi.org/10.1029/2022JE007570]
¹Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL UK
Published by arrangement with John Wiley & Sons

Rocks of the lunar granulite suite are the product of high-temperature metamorphism within the Moon’s crust. However, to date, their formation conditions have few constraints. Here we combine Ti-in-pyroxene element diffusion modelling and two-pyroxene thermometry with thermal modelling of the lunar crust in order to assess potential heat sources that could generate granulite metamorphism within the lunar highland crust. For the samples investigated in this study, the pyroxene crystals experienced peak metamorphic temperatures between ∼1027 and 1091 ºC over timescales ranging from ∼153 years to ∼15.1 kyrs. To best satisfy these temperature and timescale constraints, hot (∼2300 ºC) impact melt sheets with thickness ranging from 350 m to 3.35 km – equating to impact crater diameters between ∼60 and 280 km – have the potential to heat the underlying anorthositic crust. Deep (>20 km) igneous bodies, such as the large (>10 km thick) sills observed by the GRAIL mission near the base of the lunar crust, also have the potential to generate the required peak metamorphic temperatures; however, the thermal equilibration timescales in this scenario are modelled to be much larger (> 100 kyrs) than was witnessed by the granulites investigated. Our modelling highlights that, while lunar granulites are only a minor component within the Apollo and meteorite collection, they are likely an important and ubiquitous lithology within the lunar highland crust.

^1,2M. Darby Dyar,¹Sydney M. Wallace,^1,2Thomas H. Burbine,³Daniel R. Sheldon
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2023.115718]
¹Planetary Science Institute, 1700 East Fort Lowell, Suite 106, Tucson, AZ 85719-2395, USA
²Department of Astronomy, Mount Holyoke College, 50 College St., South Hadley, MA 01075, USA
³College of Information and Computer Sciences | 140 Governors Dr., Amherst, MA 01003, USA
Copyright Elsevier

Understanding the distribution of matter within our Solar System requires a robust methodology for evaluating the composition of small objects in the asteroid belt. Existing asteroid taxonomies have variously been based on spectral features relating to mineralogy and on classification of asteroid spectra alone. This project tests a fundamentally different approach, using machine learning algorithms to classify asteroids based on spectroscopic characteristics of existing meteorite classes. After evaluating four classification techniques built on labeled meteorite spectral data, logistic regression (LR) was determined to provide the most accurate results that distinguish eight robust groups of meteorite classes to which asteroid spectra can then be matched. The groups are rooted in mineralogical composition and directly relate meteorites to potential host bodies. A standalone LR algorithm classifies unknown asteroid spectra uniquely as one of eight specific group, allowing the distribution of compositions in the asteroid belt to be evaluated.