In this project I will use Bayesian techniques to determine the properties of stars (including their distance from us) based on information taken from their spectra and colours (which tell us about their intrinsic brightness) and their observed brightness.
The stars are all part of our Galaxy, the Milky Way. Learning the distances to stars is remarkably difficult, but essential for learning about the properties of the Milky Way. Without this information we cannot interpret anything else we know about the star in the context of the galaxy as a whole.
Most of the stars analysed are ones observed by the RAVE spectroscopic survey. This a survey of 500 000 stars, carried out at the Australian Astronomical Observatory. RAVE data have been used to investigate the dark matter content of the Milky Way, the kinematics of the stellar component of the Milky Way including the effect of the Milky Way’s central bar and spiral arms, and the chemical composition of the Milky Way. None of this work would have been possible without distance estimates to stars.
The RAVE survey includes the largest available set of spectroscopic observations of stars which had. Parallaxes are an alternative, direct, method of estimating the distances to stars. The contents of Gaia's data release have been driven by work performed at Lund Observatory. Combining the Gaia results with the distance estimation techniques previously used will allow us to determine the distances to these stars with unprecedented precision, and teach us more about the intrinsic properties of the stars. This will allow us unprecedented insight into the nature of the Milky Way.
In addition, I will apply these techniques to stars observed by the APOGEE spectroscopic survey (http://www.sdss.org/surveys/apogee/), which surveys different regions of the Milky Way galaxy.
The techniques used in this project are an extension of those described by Binney et al (2014, MNRAS, 437, 351) and McMillan et al (2018, MNRAS, 477, 5279) - a paper which made use of the resources allocated under grant SNIC 2016/4-17.