  The underlying structure of matter can be deeply probed via precision
measurements of the mass of the \emph{top quark}, the most massive observed
fundamental particle. Top quarks can be produced and studied only in collisions
at high energy particle accelerators. Most collisions, however, do not produce
top quarks; making precise measurements requires culling these collisions into
a sample that is rich in collisions producing top quarks (\emph{signal}) and
spare in collisions producing other particles (\emph{background}). Collision
selection is typically performed with heuristics or supervised learning
methods. However, such approaches are suboptimal because they assume that the
selector with the highest classification accuracy will yield a mass measurement
with the smallest statistical uncertainty. In practice, however, the mass
measurement is more sensitive to some backgrounds than others. Hence, this
paper presents a new approach that uses stochastic optimization techniques to
directly search for selectors that minimize statistical uncertainty in the top
quark mass measurement. Empirical results confirm that stochastically optimized
selectors have much smaller uncertainty. This new approach contributes
substantially to our knowledge of the top quark's mass, as the new selectors
are currently in use selecting real collisions.
