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\begin{document}

\title{Machine Learning as a Tool to Aid in the Interpretation of Spectroscopic
Data: Applications to Lunar and Planetary Exploration}



\author[1]{Prabhakar Misra}%
\author[2]{Dina Bower}%
\author[3]{Robert Coleman}%
\affil[1]{NASA Goddard Space Flight Center}%
\affil[2]{University of Maryland College Park}%
\affil[3]{Howard University}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
The precise spectroscopic identification of mineral polytypes and
specific organic molecules is key to understanding planetary processes
and the potential for life beyond Earth in the solar system. For in situ
exploration, Raman spectroscopy has been chosen for the NASA Mars
Perseverance Rover and upcoming ESA ExoMars missions because it is an
information-rich, non-contact, non-destructive method for identifying
and characterizing compounds. Misinterpretation of Raman spectra can
result in the misidentification of key information used to reconstruct
environmental regimes or the detection of potential biosignatures.
Machine learning can provide a means to disentangle the mixed signatures
that occur in spectra from heterogenous targets by building algorithms
capable of discerning subtle differences. Here we discuss an approach
that incorporates a Matlab-based machine learning algorithm to study
individual mineral samples as a starting point for more complex
algorithms targeted for rocks and sediments. The present study focuses
on Raman spectroscopy using visible (VIS) excitation laser (514 nm and
532 nm) and a near IR (NIR) excitation laser (at 780 nm) of an
assortment of mineral samples typical for rocks on Mars and the Moon,
namely olivines, three types of plagioclase minerals (anorthite,
bytownite, labradorite), and pyroxenes (augite and enstatite). We have
also begun to study the effect of temperature on the vibrational modes
for the same mineral samples over a temperature range 300 -- 473 K under
NIR excitation. Our preliminary data show, for example, that olivine
samples from two different locations may exhibit the same typical
symmetric and asymmetric stretch and bending vibrations for forsterite
(Mg\textsubscript{2}SiO\textsubscript{4}); however, under increasing
temperatures the peak intensities of \textasciitilde{} 820
cm\textsuperscript{-1} and \textasciitilde{} 845 cm\textsuperscript{-1}
features exhibited by each sample differed. Our results also showed an
enhancement of the Raman peak intensity for plagioclase samples as the
temperature increased up to 373K, but a decrease at temperatures beyond
that. *Acknowledgments: P. Misra and R. Coleman, Jr. acknowledge support
from NASA Award \# 80NCCS20M0019, NSF Award \# PHY 1950379 \& Howard
University IDCR \# U100189; and D. Bower would like to acknowledge the
support of the Internal Research and Development and Fundamental
Laboratory Research Programs at NASA Goddard.%
\end{abstract}%



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