The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time
This work addresses the need for improved anomaly detection in astronomical surveys, though it appears incremental as it builds on existing autoencoder methods for a specific domain.
The paper tackles the problem of detecting anomalies in Solar System object data for the Legacy Survey of Space and Time by training a deep autoencoder, resulting in the identification of interesting examples like interstellar objects and demonstrating the method's efficacy for finding further anomalies.
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.