Speaker
Luigi DelDebbio
(University of Edinburgh)
Description
ML techniques are routinely used to solve Inverse Problems in particle physics. Focussing on the case of PDF fitting as a testbed, we develop a description of the training dynamics during gradient descent. Our analysis shows that the ML solution is one way of regulating an otherwise ill-defined problem and allows us to address the role of hyperparameters in the training process. A critical comparison with other regulators will yield a better understanding of systematic errors (misspecification) in the different fitting procedures.