Domain Generalization with Machine Learning in the NOvA Experiment

The book "Domain Generalization with Machine Learning in the NOvA Experiment" by Andrew T.C. Sutton provides a comprehensive examination of the application of neural networks for the analysis of neutrinos. It highlights the challenges and advancements in the field of machine learning, particularly regarding the identification of particle types and the determination of their energies in detectors used in the NOvA neutrino experiment. This experiment investigates the changes in a neutrino beam that travels approximately 800 km through the Earth. A central theme of the book is the necessity of accounting for systematic uncertainties in the simulations, which are crucial for analyzing the experimental data. The work presents the first application of adversarial domain generalization in high-energy physics to enhance the robustness of NOvA analyses and improve the significance of the experimental results.