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\begin{document}

\title{Algorithmic Classification of Raman Spectra Biosignatures: Improving
Life Detection Confidence}



\author[1]{Tao Sheng}%
\author[2]{Aarya Mishra}%
\author[3]{Sunanda Sharma}%
\author[4]{Jesse Murray}%
\author[5]{Diana Gentry}%
\affil[1]{University of Pittsburgh}%
\affil[2]{University of San Francisco}%
\affil[3]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[4]{University of Oxford}%
\affil[5]{NASA Ames Research Center}%


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  \date{May 18, 2024}


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\begin{abstract}
Since the famously inconclusive Viking missions, we have observed an
increased desire to discover life outside the Earth. However, if we are
to plan effective life-detection missions, then we must meet the
challenge of classifying potential ``agnostic'' biosignatures
(indicators of life or the absence of life). Agnostic refers to
attempting to not use biosignatures that would bias towards Earth
centric life standards, which would be ``putting the answer in the
question.'' Machine learning techniques, specifically statistical
classification already showed promising results in other fields. Applied
to astrobiology, it may provide clarity on how different and independent
measurements of the same biosignature affects your confidence in whether
it is indicative of life. In this work, these algorithms were
implemented to classify Raman spectra of potential biosignatures. Data
was collected from public databases and individual research papers,
processed, and then evaluated with several different algorithms. After
thousands of simulations to allow the algorithms to test their
classifications, we observed an 81\% probability of correct
classification when all the algorithms' individual predictions were
combined. These results demonstrate Raman spectroscopy's potential for
life-detection missions, and ability to improve upon a qualitative
criterion for identifying indicative of life biosignatures.%
\end{abstract}%



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