  Previous studies have demonstrated the utility and applicability of machine
learning techniques to jet physics. In this paper, we construct new observables
for the discrimination of jets from different originating particles exclusively
from information identified by the machine. The approach we propose is to first
organize information in the jet by resolved phase space and determine the
effective $N$-body phase space at which discrimination power saturates. This
then allows for the construction of a discrimination observable from the
$N$-body phase space coordinates. A general form of this observable can be
expressed with numerous parameters that are chosen so that the observable
maximizes the signal vs.~background likelihood. Here, we illustrate this
technique applied to discrimination of $H\to b\bar b$ decays from massive $g\to
b\bar b$ splittings. We show that for a simple parametrization, we can
construct an observable that has discrimination power comparable to, or better
than, widely-used observables motivated from theory considerations. For the
case of jets on which modified mass-drop tagger grooming is applied, the
observable that the machine learns is essentially the angle of the dominant
gluon emission off of the $b\bar b$ pair.
