\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Leveraging Machine Learning Approaches to Predict Organic Carbon
Abundance in Mars-Analog Hypersaline Lake Sediments}



\author[1]{Floyd Nichols}%
\author[2]{Alexandra Pontefract}%
\author[1]{Andrew L Masterson}%
\author[1]{Mia L Thompson}%
\author[3]{Christopher Carr}%
\author[1]{Mia T Tuccillo}%
\author[1]{Magdalena Osburn}%
\affil[1]{Northwestern University}%
\affil[2]{Johns Hopkins University}%
\affil[3]{Georgia Institute of Technology}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Modern advancements in laboratory and instrumental techniques in
astrobiology have improved our life detection capabilities on both Earth
and beyond. These advancements have also increased the complexity of
data often resulting in datasets that are characterized by complex and
non-linear relationships. Machine learning methods are underutilized in
astrobiology; however, these methods are extremely effective at
revealing structure and patterns in complex datasets when paired with
the right algorithms. Here, we employ a series of classification and
regression algorithms to predict the abundance of organic carbon (OC)
from X-ray fluorescence (XRF) data in dynamic Mars-analog hypersaline
lake sediments. More specifically, we constructed models using the
random forest (RF), k-nearest neighbors (KNN), support vector machine
(SVM), and logistic regression (LR) algorithms. Overall, our trained
models showed good performance with predicting the abundance of OC, with
accuracies from 80\% to 94\%. Our results show how applying predictive
models to astrobiology datasets can help life detection efforts. Machine
learning approaches such as classification and regression algorithms
offer insight into complex data while providing agnostic insights,
ultimately creating a more efficient search for OC. We applied our
trained model on XRF data from Martian soil using PIXL and Odyssey
datasets to produce probability predictions of OC abundance. Our
predictions show a high probability that OC abundance is low which is
comparable to OC data from recently landed missions. These results
highlight the potential for machine learning models to be trained on
data from analog environments on Earth and then transferred (transfer
learning) to extraterrestrial targets.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

