Learning from data in fundamental physics research
Bayesian statistics allows to quantify the strength of inductive inference from facts (such as experimental data) to propositions such as scientific hypotheses and models. It can be used for example in searches for physics beyond the standard model, and in multi-message astronomy for the analysis of gravitational wave signals. In our theoretical-physics research we develop novel machine-learning methods to make the Bayesian approach tractable for models that involve heavy computing. A general message of this presentation will be that the Bayesian philosophy can be embraced by machine-learning practitioners to introduce uncertainties and to avoid overfitting.
Christian Forssén is Professor in theoretical physics at Chalmers. He uses high-performance computing, Bayesian statistics and machine learning, to address basic-science questions within theoretical nuclear and particle physics. Christian has supervised research projects for more than 100 bachelor and master students and has been teaching both undergraduate and graduate-level physics classes, as well as advanced courses on Bayesian data analysis and the use of machine learning methods in physics.
