A series of curent and upcoming space missions have targeted the brightest few million stars with unprecedented precision. The Gaia mission is measuring stellar positions to an accuracy ~100 times better than the previous state of the art (HIPPARCOS), while NASA's TESS mission will find most of the nearby transiting exoplanets and measure how fast stars spin accross the sky. Stellar parameters through spectroscopy will soon also be released from the Gaia mission, and several large international spectroscopic surveys are soon to begin.  

In this context, our team has developed Chronostar (code on GitHub)- a Bayesian technique to model young stellar associations and determine which nearby stars are part of them. Young associations are groups of stars that are born together (i.e. like a cluster) but then gradually move apart after the star forming gas disperses. By modelling how the initial distributions of stars move in the Galactic gravitational potential, we can combine all available data to robustly determine membership probabilities for individual stars to young associations, even if the associations were previously unknown. The most exciting part of Chronostar is, in principle, the ability to find adolescent stars - stars that are young but not so young that their youth is betrayed by bright emission lines.

There are several directions this project could take, depending on your interests:

• A focus on computational aspects of the project, so that we can use the algorithms on millions of stars at once for ~100 different association sub-components.
• Adding colour and spectroscopic information to the input data sets.
• Using the code as is to new regions of the sky, to find new young stars.