The unprecedented depth, cadence, and volume of LSST data presents a unique opportunity for serendipitous discoveries by identifying rare or unexpected phenomena ('anomalies'), and will necessitate the application of clever analytical techniques borrowed from the field of data science. In this session we will discuss computational techniques to identify anomalies, and state-of-the-art applications of machine learning (ML) and artificial intelligence (AI) software.

Contributed talks:

• S31: Oleksandra Razim - Searching for anomalous variability with LSST and friends
• S63: Siddharth Chaini - Anomaly hunting with Distance Metrics for LSST
• S68: Willow Fox Fortino - SNIHIL: A New Spectroscopic Supernova Classifier

Go to the list of all contributed abstracts.

Agenda:

Agenda TBC.