  The Interstellar Boundary Explorer (IBEX) satellite collects data on
energetic neutral atoms (ENAs) that provide insight into the heliosphere, the
region surrounding our solar system and separating it from interstellar space.
IBEX collects information on these particles and on extraneous ``background''
particles. While IBEX records how and when the different particles are
observed, it does not distinguish between heliospheric ENA particles and
incidental background particles. To address this issue, all IBEX data has
historically been manually labeled as ``good'' ENA data, or ``bad'' background
data. This manual culling process is incredibly time-intensive and contingent
on subjective, manually-induced decision thresholds. In this paper, we develop
a three-stage automated culling process, called LOTUS, that uses random forests
to expedite and standardize the labelling process. In Stage 1, LOTUS uses
random forests to obtain probabilities of observing true ENA particles on a
per-observation basis. In Stage 2, LOTUS aggregates these probabilities to
obtain predictions within small windows of time. In Stage 3, LOTUS refines
these predictions. We compare the labels generated by LOTUS to those manually
generated by the subject matter expert. We use various metrics to demonstrate
that LOTUS is a useful automated process for supplementing and standardizing
the manual culling process.
