Detecting and tracking Near-Earth Objects

The Centre for Doctoral Training in Data Intensive Science is a STFC-funded PhD training programme in physics and astronomy, delivered by a consortium comprising of the Universities of Cardiff (lead), Bristol and Swansea.

Our aim is to develop a training ground for the next generation of data scientists required by both industry and academia.

Based in the three centres and working closely with UK industry, our 4 year PhD in Data Intensive Science programme offers a unique opportunity to develop advanced knowledge of data intensive methods. The programme combines specialist training in data intensive science, transferable skills development and a 6 month industrial placement.

This will enable you to address scientific questions across particle physics, gravitational physics and astronomy, and provide opportunities to apply this knowledge to cutting edge problems in physics and astronomy, as well as industry.

About the research
Increasing our chances of survival on the Earth.

Recent work indicates that Earth-threatening asteroids, comets and meteors - collectively Near-Earth Objects (NEOs) - are considerably more numerous than previously thought. Existing surveys have detected most of the larger objects ≥ 1 km, but detection completeness drops precipitously for objects smaller than about 100 m, and catastrophic damage could be produced by even smaller objects, most of which are currently undetectable.

The ability to detect and track, well in advance, small moving objects in the Solar System may therefore be crucial to our survival. An inventory of Solar System objects is one of the principal science drivers for the Large Synoptic Survey Telescope (LSST), but the currently proposed processing techniques, utilising the Moving Object Pipeline System (MOPS) cannot do full justice to the data.

This project will develop a version of the Point Process Algorithm (PPA) that includes a dynamical term involving the Fokker-Planck Operator, and thereby combines the detection and tracking terms to attain much greater sensitivity. It will then apply this procedure to the data from PanSTARRS, NEOWISE, and LSST, to build a comprehensive catalogue of NEOs and their trajectories, which is continuously updated to incorporate new data, as soon as it becomes available. The student will learn to handle very large data sets, and to analyse them statistically.