One of the most exciting promises of LSST is its potential to discover completely novel phenomena, never before observed or predicted from theory.  Serendipitous discovery in astrophysics has traditionally tapped heavily into person-power, with citizen-scientist platforms (e.g. Hanny’s Voorwerp, Boyajian star). However, with 2x3.2 gigapixels images/minute for 10 years, the discovery of unusual phenomena in LSST images is a tremendous, unsolved challenge that cannot rely uniquely on serendipity (simple scalings from the Galaxy Zoo indicates the entire world population would be insufficient for this approach!). In this session, we will talk about how we can force serendipitous discovery in LSST by developing and applying machine-learning and artificial intelligence-aided anomaly detection techniques and how the Rubin community can tap on knowledge developed in other fields, including cybersecurity and the medical field.

Agenda: 
Federica Bianco: Rules of engagement, intro, TVS anomalies subgroup (<=7 min)

Ashish Mahabal: new ISSC anomaly detection interest group (<=7 min)

Xiaolong Li: Time Domain perspective 

Will Clarkson: Proper Motion perspective 

Ari Heinz: Solar System Perspective 

Agnieszka Pollo: Extragalactic perspective=

Konstantin Malanchev: Detection of anomalies in ZTF with SNAD 

Ashley Villar: Detection of anomalies in ZTF with AI

1 sliders:
Chris Lintott; Ashish Mahabal; Abi Saha; Patrick Aleo;

Discussion: what is the anomaly you think LSST will not be able to detect?