¹A. Emran, ¹K.M. Stack
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2025.116576]
¹NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Copyright Elsevier

Hollows on Mercury are small depressions formed by volatile loss, providing important clues about the volatile inventory of the planet’s surface and shallow subsurface. We investigate the composition of hollows in various phases of devolatilization at Dominici crater. By applying a machine learning approach to MESSENGER Mercury Dual Imaging System data, we defined surface units within the study area and extracted their reflectance spectra. We applied linear (areal) spectral modeling using laboratory sulfides, chlorides, graphite, and silicate mineral spectra to estimate the composition of hollows and their surrounding terrains. At Dominici, the hollow on the crater rim/wall is interpreted to be active, while that in the center of the crater is interpreted as a waning hollow. We find that the active hollow predominantly comprises silicates (augite and albite), with a trace amount of graphite and CaS. In contrast, waning hollows contain marginally elevated sulfides (MgS and CaS) and graphite, but slightly lower silicates than the active hollow. The spectra of low reflectance terrain surrounding the hollows appear to be dominated by graphite and sulfides, which contribute to its darker appearance. We suggest that hollow at the crater forms due to thermal decomposition of sulfides, primarily MgS possibly mixed with CaS, as well as possible the depletion of graphite. As devolatilization wanes, a mixture of predominantly silicate minerals remains in the hollows — impeding further vertical growth.

^1,2Roshan Nath et al. (>10)
Meteoritics & Planetary Science (in Press) Link to Article [https://doi.org/10.1111/maps.14342]
¹Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, India
²Physical Research Laboratory, Ahmedabad, Gujarat, India
Published by arrangement with John Wiley & Sons

Meteorites, remnants of asteroids that successfully survive their passage through the Earth’s atmosphere, hold critical information about the evolution and history of the solar system. Traditional methods of analyzing these rare and precious specimens often involve destructive geochemical techniques, which deplete the sample and limit subsequent analyses. The accurate classification of meteorites, typically determined through petrological examination, is crucial before any further analytical steps. Reflectance spectroscopy, which interprets a sample’s characteristics by analyzing reflected light, has emerged as a nondestructive alternative with significant potential for meteorite classification. In this technique, apparently, sometimes we do not need to process the sample. This technique allows for the examination of spectral features such as absorption bands, symmetry, band centers, inflection points, and overall slope. In this study, we employed spectral reflectance data from 1781 meteorite samples to develop and fine-tune a deep learning model capable of accurate classification. The model was trained on 75% of the dataset and validated on the remaining 25%, achieving a validation accuracy of 93%. These results demonstrate the efficiency of using deep learning and reflectance spectroscopy for meteorite classification, offering a nondestructive and accurate alternative to traditional methods.