The fields of planetary sciences and exoplanets have firmly entered the era of ‘big data’.
The fields of planetary sciences and exoplanets have firmly entered the era of ‘big data’. Current and future space missions provide an unprecedented wealth of information. Analysing currently available observations of often invoke large and correlated parameter spaces that can be difficult to map or constrain. This is true for both: the data analysis of observations as well as the theoretical modelling. Issues of low signal-to-noise data and large, non-linear parameter spaces are nothing new and commonly found in many fields of engineering and the physical sciences. Recent years have seen vast improvements in statistical data analysis and machine learning that have revolutionised fields as diverse as telecommunication, pattern recognition, medical physics and cosmology. In many aspects, data mining and non-linearity challenges encountered in other data intensive fields are directly transferable to the field of planetary sciences. UCL is at the forefront of this effort with both, the Centre for Space Exochemistry Data and the European Research Council funded ExoAI group. Together, working with leading statisticians, we develop novel machine learning solutions to these the contemporary data problems.
ExoAI - Deciphering super-Earths using Artificial Intelligence, ERC Starting Grant 173948 (PI: Waldmann)