The data perspective on chiral effective field theory

Doctoral thesis, 2017

The scientific method implies a dynamical relationship between experiment and theory. Indeed, experimental results are understood through theories, which themselves are of less value until confronted with experiment. In this thesis I study this relationship by quantifying two key properties of theories: theoretical uncertainties and predictive power. Specifically I investigate chiral effective field theory and the precision and accuracy by which it reproduces and predicts low-energy nuclear observables. I estimate both statistical and systematic uncertainties. The conclusion is that the latter, which in my approximation originates from omitted higher-order terms in the chiral expansion, are much larger than the former. In relation to this, I investigate the order-by-order convergence up to fourth order in the chiral expansion. I find that predictions generally improve with increasing order, while the additional low-energy constants (LECs) of the interaction makes it more difficult to fully constrain the theory. Furthermore, in order to accurately reproduce properties of heavier nuclei I see indications that it is necessary to include selected experimental data from such systems directly in the fitting of the interaction. In order to perform these studies I have developed accurate and efficient methods as well as computer codes for the calculation of observables. In particular, the application of automatic differentiation for derivative calculations is shown to be crucial for the minimization procedure. These developments open up new avenues for future studies. For example, it is now possible to do extensive sensitivity analyses of the experimental data and the model; to investigate the power counting from a data perspective; and incorporate more experimental data in the fitting procedure.