Gaia, a space satellite mission operating out of ESA, will chart a 3D map of the Milky Way to unravel our Galaxy’s formation and evolution.
Over its 5-year mission, Gaia (http://sci.esa.int/gaia/) will measure the positions and velocities of about 1 billion stars, or about 1% of the stars in our Galaxy. Some of these measurements will be very precise, and others less confident. Using a data driven model of stars, we can increase the precision of these measurements, as well as find interesting physical structures blurred out by the less precise data.
Lauren Andreson will present her most recent work measuring more precise distance to local stars in the first data release of Gaia, as well as some thoughts on measuring distances to stars that have no astrometric measurements.
This talk is for anyone interested in astronomy, the Milky Way, astronomical data analysis, and statistical methods
Lauren Anderson is a postdoctoral fellow at the Flatiron Institute in NYC. She currently works on measuring distances to stars in the Milky Way using data from the satellite mission Gaia, a mission which will chart the 3D map of our Galaxy. In general, she's interested in statistical methods and machine learning algorithms applied to large astronomical data sets. She recently graduated from the University of Washington with a Ph.D. in Astronomy, and prior to that, she earned a B.A. in Physics and Astronomy from the University of California, Berkeley. She misses the west coast dearly.
