An information theoretic framework for classifying exoplanetary system architectures. (arXiv:2003.11098v1 [astro-ph.EP])

An information theoretic framework for classifying exoplanetary system architectures. (arXiv:2003.11098v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Gilbert_G/0/1/0/all/0/1">Gregory J. Gilbert</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Fabrycky_D/0/1/0/all/0/1">Daniel C. Fabrycky</a>

We propose several descriptive measures to characterize the arrangements of
planetary masses, periods, and mutual inclinations within exoplanetary systems.
These measures are based in complexity theory and capture the global,
system-level trends of each architecture. Our approach considers all planets in
a system simultaneously, facilitating both intra-system and inter-system
analysis. We find that based on these measures, Kepler’s high-multiplicity
($Ngeq3$) systems can be explained if most systems belong to a single
intrinsic population, with a subset of high-multiplicity systems ($sim20%$)
hosting additional, undetected planets intermediate in period between the known
planets. We confirm prior findings that planets within a system tend to be
roughly the same size and approximately coplanar. We find that forward modeling
has not yet reproduced the high degree of spacing similarity (in log-period)
actually seen in the Kepler data. Although our classification scheme was
developed using compact Kepler multis as a test sample, our methods can be
immediately applied to any other population of exoplanetary systems. We apply
this classification scheme to (1) quantify the similarity between systems, (2)
resolve observational biases from physical trends, and (3) identify which
systems to search for additional planets and where to look for these planets.

We propose several descriptive measures to characterize the arrangements of
planetary masses, periods, and mutual inclinations within exoplanetary systems.
These measures are based in complexity theory and capture the global,
system-level trends of each architecture. Our approach considers all planets in
a system simultaneously, facilitating both intra-system and inter-system
analysis. We find that based on these measures, Kepler’s high-multiplicity
($Ngeq3$) systems can be explained if most systems belong to a single
intrinsic population, with a subset of high-multiplicity systems ($sim20%$)
hosting additional, undetected planets intermediate in period between the known
planets. We confirm prior findings that planets within a system tend to be
roughly the same size and approximately coplanar. We find that forward modeling
has not yet reproduced the high degree of spacing similarity (in log-period)
actually seen in the Kepler data. Although our classification scheme was
developed using compact Kepler multis as a test sample, our methods can be
immediately applied to any other population of exoplanetary systems. We apply
this classification scheme to (1) quantify the similarity between systems, (2)
resolve observational biases from physical trends, and (3) identify which
systems to search for additional planets and where to look for these planets.

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