Statistically Learning Dispersed New Physics at the LHC

Particle physics aims to find a description of the laws that govern our universe at its tiniest scales (sub-atomic particles). To that end, a massive collaboration of a few thousand particle physicists, including ourselves, have joined forces to build the world`s largest microscope, the 30-kilometer long Large Hadron Collider (LHC). Since the LHC has started running in 2009, a massive amount of approximately 1 exabyte (1 billion gigabytes) of data has been accumulated. This data and its possible interpretations are documented in hundreds of scientific publications. But what do all these publications truly tell us about physics beyond the known? Important questions that remain open are whether there are indications that our universe is supersymmetrical. Can we rule out the presence of more than three (space) plus one (time) spacetime dimensions? Do we suspect hints of the existence of dark matter in the data? And if so, do these only become apparent when combining many individual results to a non-trivial global interpretation of the data? Finding answers to these questions is a formidable but not impossible task. In this Austro-French bilateral project, we try to tackle these questions from a different, novel angle. In previous, preliminary work we developed an artificial intelligence (AI), a software program that sieved through around one hundred published results, to look for clues to new, unknown physics that would only become apparent when many results are combined. In the current project, we try and develop this idea to full maturity. The artificial intelligence is to be expanded and "fed" with a larger part of the LHC results. At the same time, it will also independently develop "protomodels" - potential, incomplete precursor theories of a new, fundamental physical theory. If the AI finds something in the course of this project, we humans should try to infer a full-fledged, meaningful physical theory from these protomodels. Thus, we are recruiting an AI for our mission. A symbiosis of the respective strengths of artificial and human intelligence joined together in the pursuit of a more fundamental description of the energy and matter in our universe.