The Zwicky Transient Facility (ZTF) is a 47 square degree field of view time-domain survey from the Samuel Oschin 48-inch Schmidt telescope at Palomar Observatory in California. DR2 was released recently (Dec 2019) and boasts of 100 billion source detections in g/r/i filters combined.
I lead the machine learning (ML) for the survey, and the team works on various classification aspects e.g. separating real-bogus sources, identifying streaking asteroids, pigeon holing sources in to their subclasses using advanced statistical and computational methods.
The Catalina Sky Survey (CSS) NEO project uses three dedicated telescopes to cover thirty three thousand square degrees (now two in Arizona). The Catalina Real-Time Transient Survey (CRTS) utilized the CSS data to search for rare and interesting transients and variables. Currently the transients are not actively screened.
I worked on classification of transients as well as building some initial infrastructure.
keyword crts keyword catalina --
The Large Synoptic Survey Telescope (LSST) is a project that will begin observing in Oct. 2022 from a 8.4m telescope in Chile. Over 10 years it will obtain 1000 observations in 6 filters generating 15 TB data nightly.
I was a co-chair of the Transient and Variable Source (TVS) Science Collaboration for a few years, and I am part of the Informatics and Statistics SC. The alert brokers for LSST are currently training on the alerts produced by ZTF.
Automatic Learning for the Rapid Classification of Events (ALeRCE) is an effort for the classification of astronomical alerts. A majority of the members are from Chile, and as the ALeRCE team gets ready for LSST, they are using the ZTF alert stream for training models and algorithms.
As part of CRTS and ZTF I have contributed some labeled data, and am loosely involved in the machine learning and classification effort of the group through collaborative meetings and through teaching at the La Serena Schools on Data Science (partly funded by NSF).