About the project
When exploring the smallest fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data, for example from the Large Hadron Collider or astrophysical observations, we need to perform very computationally expensive calculations in quantum field theory (QFT); calculations that are expensive due to the increasing complexity of higher-order quantum corrections. These are today too slow to perform at the necessary precision except for in the simplest models.
At the same time, the interpretation of new models given the available data, finding the best-fit regions of their parameter spaces, and making comparisons of different models with each other through their goodness-of-fit, is made computationally intractable due to the size of the parameter spaces of realistic models and the complexity of the likelihood evaluation for each model.
Solutions to these problems can not be found in physics alone. This project builds on an interdisciplinary collaboration between physicists and statisticians focused on statistical learning and inference problems in high-energy physics. The project will develop machine learning based regression techniques to speed up QFT calculations with a proper probabilistic interpretation of uncertainties from higher-order contributions, it will develop a continual learning framework for faster emulation of the likelihood for model parameters, and it will investigate new improved statistical approaches to the problems of best-fit and goodness-of-fit using these emulations. In short, the mission of the project is to make the basic PLUMBIN' (Physics Learning Using Machines and Bayesian INference) for future high-energy physics discoveries.
Financing
This project is financed by the Research Council of Norway (RCN) in the period 2022-2028 through a FRIPRO grant (323985) with Are Raklev as Principle Investigator.