Machine Learning is quickly becoming a popular method to analyze astronomical data. There is a great deal of interest among the astronomical community in the powerful techniques that are now being developed, with every session, workshop, or seminar relating to the subject having overflow audiences.
We are therefore organizing a ML-oriented special session at AAS 233. The goal of this session is to focus attention on new ML applications specific for astronomical data. Under the principle that it is better to learn with concrete examples, we seek to provide a forum for reporting on new applications and enhancements in existing methodologies. Modern telescopes collect a large amount of data, freely accessible via archives, to all scientists. With big datasets, come big opportunities. The SDSS, Kepler, and K2 datasets, the recently released Gaia DR2, the forthcoming LSST in the optical, ALMA, MWA, and SKA in the radio, SDO in the EUV, are perfect illustrations of the power of data to unlock new science. This session is designed to help us prepare to take advantage of these opportunities, by making astronomers aware of both the promise of ML and to understand its limitations.
Beyond astronomy, ML has many applications in science and a wide range of other fields. The skills developed by astronomers as they investigate and implement ML techniques will also serve them in cross-disciplinary endeavours, and will be an excellent way for Astro grad students to enhance their skill sets for non-astronomy career paths.
Our session will consist of a talk by Mario Juric (DIRAC Institute), focused on transient filtering and on knowledge discovery to unearth interesting science, for example, in data to be collected by LSST, followed by a talk by James Davenport (DIRAC Institute) focused on the practical aspects of learning and applying ML concepts to Kepler and Gaia data. This will be followed by up to four contributed talks (10 minutes each) which we will select from the submitted abstracts.
Chair: TBD (AFFIL)
Vinay Kashyap (vkashyap @ cfa . harvard . edu) Rosanne Di Stefano (rdistefano @ cfa . harvard . edu) Aneta Siemiginowska (asiemiginowska @ cfa . harvard . edu)