Domain Generalization with Machine Learning in the NOvA Experiment

Chapter 1: Neutrinos: A Desperate Remedy.- Chapter 2. A Review of Neutrino Physics.- Chapter 3. The NOvA Experiment.- Chapter 4. Event Reconstruction.- Chapter 5. The 3-Flavor Analysis.- Chapter 6. A Long Short-Term Memory Neural Network.- Chapter 7. Domain Generalization by Adversarial Training.- Chapter 8. Conclusion.

I am an experimental particle physicist focusing on neutrino physics as part of the NOvA and ANNIE experiments located at the Fermi National Accelerator Laboratory (Fermilab) in Batavia Illinois, USA. After graduating cum laude from the University of North Carolina at Charlotte with scientific bachelor degrees in Mechanical Engineering and Physics, I went on to pursue my Ph.D. at the University of Virginia in Charlottesville Virginia. Under the supervision of Craig Group, I studied neutrino physics as a member of the NOvA collaboration. Putting my engineering degree to good use, I received the US Department of Energy Office of Science Graduate Student Research Award to travel to Fermilab and assist in the construction of a Test Beam experiment for NOvA. Alongside the NOvA Test Beam, I also contributed to the main 3-flavor oscillation analysis and was selected as part of the three-person writing committee to draft the paper summarizing our 2020 results (M.A Acero et al. 2022, doi: 10.1103/PhysRevD.106.032004). My graduate education culminated in the machine learning project detailed in this book, which focuses on a technique to train more robust neural networks and reduce the impact of systematic uncertainties that limit the precision of our measurements.