English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 deepautoencoder for anomaly detection and use the learned latent space to searchfor other interesting objects. We demonstrate the efficacy of the autoencoderapproach by finding interesting examples, such as interstellar objects, andshow that using the autoencoder, further examples of interesting classes can befound. We also investigate the limits of classic unsupervised approaches toanomaly detection through the generation of synthetic anomalies and evaluatethe feasibility of using a supervised learning approach. Future work shouldconsider expanding the feature space to increase the variety of anomalies thatcan be uncovered during the survey using an autoencoder.

The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time