  Our understanding of the Universe has profited from deliberate, targeted
studies of known phenomena, as well as from serendipitous, unexpected
discoveries, such as the discovery of a complex variability pattern in the
direction of KIC 8462852 (Boyajian's star). Upcoming surveys, such as the Vera
C. Rubin Observatory Legacy Survey of Space and Time (LSST), will explore the
parameter space of astrophysical transients at all time scales, and offer the
opportunity to discover even more extreme examples of unexpected phenomena. We
investigate strategies to identify novel objects and to contextualize them
within large time-series data sets in order to facilitate the discovery of new
classes of objects, as well as the physical interpretation of their anomalous
nature. We develop a method that combines tree-based and manifold-learning
algorithms for anomaly detection in order to perform two tasks: 1) identify and
rank anomalous objects in a time-domain dataset; and 2) group those anomalies
according to their similarity in order to identify analogs. We achieve the
latter by combining an anomaly score from a tree-based method with a
dimensionality manifold-learning reduction strategy. Clustering in the reduced
space allows for the successful identification of anomalies and analogs. We
also assess the impact of pre-processing and feature engineering schemes and
investigate the astrophysical nature of the objects that our models identify as
anomalous by augmenting the Kepler data with Gaia color and luminosity
information. We find that multiple models, used in combination, are a promising
strategy to identify novel light curves and light curve families.
