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\begin{document}

\title{New Statistical Measures of Atmospheric Disequilibria and Implications
for Detecting Life and Technology}



\author[1,1]{Theresa Fisher}%
\author[1,1]{Sara Walker}%
\author[1,1]{Michael Line}%
\author[1,1]{Hyunju Kim}%
\author[1,1]{Camerian Millsaps}%
\affil[1]{Arizona State University}%


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  \date{June 15, 2024}


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\begin{abstract}
Exoplanets exhibit properties suggestive of a diversity of worlds far
exceeding that observed within our own solar system. This diversity,
combined with limited data, poses challenges for future exoplanet
characterization, especially regarding life detection: not only is the
diversity planets unprecedented, but the low resolution and s/n data
available to current and near-future technology demand we improve our
ability to infer properties of planetary atmospheres, surfaces and
potential signs of life from very little data. For this reason, recent
consensus recommendations from both within the exoplanet science
community, and without, are directing the field to move from searching
from specific products of life to developing probabilistic frameworks
for inferring the presence of life that encompass entire planetary
systems. Here, we demonstrate a new framework based on statistical
characterization of planetary atmospheric chemistries with the goal to
provide the quantitative tools required by this approach. We validate
these tools against current observational constraints available for
Jovian worlds by constructing chemical reaction networks (CRNs) from the
atmospheres of hot jupiters simulated over a wide range of temperatures
and metallicities using VULCAN. For each model, we calculated measures
of the CRN topology and more traditional measures of disequilbrium. To
model the uncertainty in observations, these properties were then used
as the basis for an interpolation function, which was then fed a series
of 10,000 point Gaussian distributions of possible initial conditions to
simulate the likelihood distribution of possible atmospheric models
centered around a specific observable such as T or metallicity. We
present results demonstrating how our multivariate, statistical approach
permits quantifying distance from disequilibrium in Jovian atmospheres.
We discuss implications for inferring the presence of life as a driver
of atmospheric disequilibrium on terrestrial worlds, and how
technologically produced molecules could influence CRN topology.%
\end{abstract}%



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