Objectives and Contextualisation

Humanity's vision of the Universe changed radically in the 20th century. The evolution of detection techniques has increased the number of objects visible in the sky from a few hundred to many billions. In addition, objects can be observed through electromagnetic radiation in a wide range of wavelengths, from radio and infrared to the visible band and X-rays. Experimental techniques from elementary particle physics have been adapted to extend observations of celestial objects, for example through higher energy photons (gamma rays). These techniques also allow, for the first time, the observation of the sky through non-electromagnetic messengers, that is, charged particles ("cosmic rays") and, very recently, neutrinos. Finally, gigantic, highly accurate laser interferometers have recently observed gravitational waves, providing another way of observing celestial objects.

All of these ways of looking at the Universe are producing enormous amounts of data that must be filtered, calibrated, analyzed, and compared with theoretical predictions. This requires data reduction in high throughput systems and simulations in high performance systems, combined with sophisticated statistical analysis and uncertainty estimation. Big Data and Artificial Intelligence techniques are being increasingly applied in the field. The objective of the course is to explore these techniques in the context of the Degree.