  The identification of top quark decays where the top quark has a large
momentum transverse to the beam axis, known as $top$ $tagging$, is a crucial
component in many measurements of Standard Model processes and searches for
beyond the Standard Model physics at the Large Hadron Collider. Machine
learning techniques have improved the performance of top tagging algorithms,
but the size of the systematic uncertainties for all proposed algorithms has
not been systematically studied. This paper presents the performance of several
machine learning based top tagging algorithms on a dataset constructed from
simulated proton-proton collision events measured with the ATLAS detector at
$\sqrt{s} = 13$ TeV. The systematic uncertainties associated with these
algorithms are estimated through an approximate procedure that is not meant to
be used in a physics analysis, but is appropriate for the level of precision
required for this study. The most performant algorithms are found to have the
largest uncertainties, motivating the development of methods to reduce these
uncertainties without compromising performance. To enable such efforts in the
wider scientific community, the datasets used in this paper are made publicly
available.
