PLUMBIN' – Developing solvents for unclogging the calculational bottleneck in high-energy physics

Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).

Credit, background: NASA's Goddard Space Flight Center/CI Lab

These are today too slow to perform at the necessary precision except in the simplest models. At the same time, the interpretation of new models is made computationally intractable due to the size of the parameter spaces of realistic models and the complexity of the likelihood evaluation. The PLUMBIN' project is a cross-disciplinary collaboration between physics and statistics focused on statistical learning and inference problems in many dimensions, aimed at making powerful new computational tools. The project will develop machine learning based regression techniques to speed up QFT calculations with a proper probabilistic interpretation of uncertainties, it will develop a continual learning framework for faster emulation of likelihoods, and it will investigate new improved statistical approaches to the problems of best-fit and goodness-of-fit using these emulations.