ABSTRACT
This conclusion presents some closing thoughts on the concepts covered in the preceding chapters of this book. The book answers one basic question: can manifestations of New Physics be unambiguously seen in a low-energy experiment. It discusses searches for indirect effects of New Physics in many systems, ranging from atomic interactions to collider physics. Machine learning (ML) is a concept that involves both algorithm building and the development of modeling tools to deal with data processing. Various ML techniques, including artificial neural networks, are now widely employed in analyses of experimental data and modeling of theoretical inputs. Explicit model building is important because both direct and indirect searches for new degrees of freedom can be done.
