Domain Generalization with Machine Learning in the NOvA Experiment

This thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which...

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Detaylı Bibliyografya
Yazar:	Sutton, Andrew T.C (Yazar)
Müşterek Yazar:	SpringerLink (Online service)
Materyal Türü:	Elektronik Ekitap
Dil:	İngilizce
Baskı/Yayın Bilgisi:	Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Edisyon:	1st ed. 2023.
Seri Bilgileri:	Springer Theses, Recognizing Outstanding Ph.D. Research,
Konular:	Particles (Nuclear physics). Particle accelerators. Measurement. Measuring instruments. Machine learning. Mathematical physics. Computer simulation. Particle Physics. Accelerator Physics. Measurement Science and Instrumentation. Machine Learning. Computational Physics and Simulations.
Online Erişim:	https://doi.org/10.1007/978-3-031-43583-6
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

İçindekiler:

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.

Domain Generalization with Machine Learning in the NOvA Experiment

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