  We present a novel method for anomaly detection in Solar System object data,
in preparation for the Legacy Survey of Space and Time. We train a deep
autoencoder for anomaly detection and use the learned latent space to search
for other interesting objects. We demonstrate the efficacy of the autoencoder
approach by finding interesting examples, such as interstellar objects, and
show that using the autoencoder, further examples of interesting classes can be
found. We also investigate the limits of classic unsupervised approaches to
anomaly detection through the generation of synthetic anomalies and evaluate
the feasibility of using a supervised learning approach. Future work should
consider expanding the feature space to increase the variety of anomalies that
can be uncovered during the survey using an autoencoder.
