  We present the construction of a novel time-domain signature extraction
methodology and the development of a supporting supervised pattern detection
algorithm. We focus on the targeted identification of eclipsing binaries that
demonstrate a feature known as the O'Connell effect. Our proposed methodology
maps stellar variable observations to a new representation known as
distribution fields (DFs). Given this novel representation, we develop a metric
learning technique directly on the DF space that is capable of specifically
identifying our stars of interest. The metric is tuned on a set of labeled
eclipsing binary data from the Kepler survey, targeting particular systems
exhibiting the O'Connell effect. The result is a conservative selection of 124
potential targets of interest out of the Villanova Eclipsing Binary Catalog.
Our framework demonstrates favorable performance on Kepler eclipsing binary
data, taking a crucial step in preparing the way for large-scale data volumes
from next-generation telescopes such as LSST and SKA.
