Charles University, Prague
Asteroids are the most numerous objects in the solar system. So far, hundreds of thousands of asteroids are known, with hundres of new discoveries every day. Altough the total number of known asteroids is large, very little is known about the physical properties of individual objects. For a significant part of the population, only the size of the bodies is known. Other physical parameters (the shape, the rotation period, direction of the rotation axis,...) are known only for hundreds of objects.
Because asteroids have in general irregular shapes and they rotate, the amount of sunlight they scatter towards the observer varies with time. This variation of brightness with time is called a lightcurve. The shape of a lightcurve depends on the shape of asteroid and also on the viewing and illumination geometry. If a sufficient number of lightcurves observed under various geometries is collected, a unique physical model of the asteroid can be reconstucted by the lightcurve inversion method.
The project Asteroids@home was started with the aim to significanly enlarge our knowledge of physical properties of asteroids. The BOINC application uses photometric measurements of asteroids observed by professional big all-sky surveys as well as 'backyard' astronomers. The data is processed using the lightcurve inversion method and a 3D shape model of an asteroid together with the rotation period and the direction of the spin axis are derived.
Because the photometric data from all-sky surveys are typically sparse in time, the rotation period is not directly 'visible' in the data and the huge parameter space has to be scanned to find the best solution. In such cases, the lightcurve inversion is very time-consuming and the distributed computation is the only way how to efficiently deal with photometry of hundres of thousands of asteroids. Moreover, in order to reveal biases in the method and reconstruct the real distribution of physical parameters in the asteroid population, it is necessary to process large data sets of 'synthetic' populations.