^1,2Kaitlyn R. Goss,^2,3Mabel L. Gray,^2,3,4Michael K. Weisberg,^2,3Denton S. Ebel
Meteoritics & Planetary Science (in Press) Link to Article [https://doi.org/10.1111/maps.13962]
¹Department of Geology, Mount Holyoke College, South Hadley, Massachusetts, USA
²Department of Earth and Planetary Sciences, American Museum of Natural History (AMNH), New York, New York, USA
³Department of Earth and Environmental Sciences, City University (CUNY) of New York Graduate Center, New York, New York, USA
⁴Department of Physical Sciences, Kingsborough Community College—CUNY, Brooklyn, New York, USA
Published by arrangement with John Wiley & Sons

We studied a thin section of Lewis Cliff (LEW) 87223, an unusual EL3-related, enstatite chondrite (EC) that has primary and secondary features not observed in other ECs. We studied its metal-rich nodules, possible shock features, and chondrules, eight of which are Al-rich chondrules (ARCs). LEW 87223 has petrologic and compositional features similar to EL3s. Enstatite is the dominant mineral; chondrule boundaries are well defined; Si content of metal (0.5–0.6 wt%) is consistent with typical EL3; it has Cr-bearing troilite, oldhamite, and alabandite; and its O-isotopic composition is similar to other ECs. However, metal abundance in LEW 87223 (~13 vol%) is slightly higher than in other EL3s and its metal nodules are texturally and mineralogically different from other ECs. Both high and low Ni metals are present, and its alabandite has higher Fe (27.8 wt% Fe) than in other EL3s. Silicates appear darkened in plane polarized light, largely due to reduction of Fe from silicate. A remarkable feature of LEW 87223 is the high abundance of ARCs, which contain Ca-rich plagioclase and varying amounts of Na-rich plagioclase along chondrule edges and as veins. This suggests Na metasomatism and the possibility of hydrothermal fluids, potentially related to an impact event. LEW 87223 expands the range of known EC material. It shows that ECs are more diverse and record a wider range of parent body processes than previously known. LEW 87223 is an anomalous EL3, potentially the first member of a new EC group should similar samples be discovered.

¹Sandeepan Dhoundiyal,^1,2Alok Porwal,³C.V. Niveditha,³Guneshwar Thangjam,¹Malcolm Aranha¹, Shivam Kumar,¹Debosmita Paul,¹R. Kalimuthu
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2023.115504]
¹Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
²Centre for Exploration Targeting, University of Western Australia, Crawley 6009, Western Australia, Australia
³National Institute of Science Education and Research, HBNI, Bhubaneswar 752050, India
Copyright Elsevier

The algorithms for mapping carbonates from Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data use the depths of the diagnostic carbonate absorption features at ~2.3 μm and ~ 2.5 μm. However, because the band depths are estimated using fixed shoulder wavelengths, subtle shifts in band centres caused by different cations in the carbonates could result in false negatives for carbonates or false positives for other minerals that have absorption features in a similar wavelength range (eg. phyllosilicates, zeolites). This paper proposes a new algorithm that is based on the following attributes of carbonate spectra in the 2.0 to 3.0 μm range: (1) presence of two diagnostic overtones features around ~2.3 μm and ~2.5 μm; however, these features may show red shift or blue shift depending on the nature of cation(s); (2) the inter band gap between ~2.3 μm and ~2.5 μm carbonate absorption features, which remains relatively constant at ~0.2 μm, even if there is a shift in the absorption features; (3) the contiguity of these two features, that is, carbonate spectra do not show any absorption features in between the above two features. The algorithm also includes a novel geometric continuum removal technique for locating the absorption features. The effectiveness of the algorithm is demonstrated using laboratory spectra, CRISM machine learning toolkit’s mineral dataset, as well as CRISM images. The true positive rate (TPR), true negative rate (TNR) and overall accuracy for the method over the CRISM machine learning toolkit’s mineral dataset are 29%, 87% and 83%, respectively.