  Inferring the properties of exoplanets from their atmospheres, while
confronting low resolution and low signal-to-noise in the context of the
quantities we want to derive, poses rigorous demands upon the data collected
from observation. Further compounding this challenge is that inferences of
exoplanet properties are built from forward models, which can include errors
due to incomplete or inaccurate assumptions in atmospheric physics and
chemistry. The confluence of observational noise and model error makes
developing techniques to identify predictive features that are robust to both
low s/n and model error increasingly important for exoplanet science. We
demonstrate how both issues can be addressed simultaneously by taking advantage
of underutilized multivariate information already present in current
atmospheric models, including thermodynamic statistics and reaction network
structure. To do so, we provide a case study of the prediction of vertical
mixing (parameterized as eddy diffusion) in hot Jupiter atmospheres and show
how prediction efficacy depends on what model information is used - e.g.
chemical species abundances, network statistics, and/or thermodynamic
statistics. We also show how the variables with the most predictive power vary
with planetary properties such as temperature and metallicity. Our results
demonstrate how inferences built on single metrics do not have utility across
all possible use cases. We also show how statistical measures derived from
network analyses tend to be better predictors when accounting for the
possibility of missing data or observational uncertainty. We discuss future
directions applying multivariate and network model information as a framework
for increasing confidence in inferences aimed at extracting features relevant
to exoplanet atmospheres and future applications to the detection of life on
terrestrial worlds.
