Exploring the Universe with SNAD: Anomaly Detection in Astronomy
This work addresses anomaly detection in astronomy for researchers, but it is incremental as it reviews and summarizes existing advancements.
The SNAD project tackles the problem of detecting astronomical anomalies in large-scale surveys using active learning and machine learning, resulting in contributions to discovering and classifying astronomical phenomena and enhancing ML techniques in astrophysics.
SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years.