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Domain generalization with machine l...

Sutton, Andrew T. C.

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Domain generalization with machine learning in the NOvA experiment

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Domain generalization with machine learning in the NOvA experiment/ by Andrew T.C. Sutton.
作者:	Sutton, Andrew T. C.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xi, 170 p. :illustrations (chiefly color), digital ;24 cm.
附註:	"Doctoral Thesis accepted by the University of Virginia, USA."
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Neutrinos. -
電子資源:	https://doi.org/10.1007/978-3-031-43583-6
ISBN:	9783031435836

Domain generalization with machine learning in the NOvA experiment
Sutton, Andrew T. C.

Domain generalization with machine learning in the NOvA experiment[electronic resource] /by Andrew T.C. Sutton. - Cham :Springer Nature Switzerland :2023. - xi, 170 p. :illustrations (chiefly color), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral Thesis accepted by the University of Virginia, USA."

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.

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 studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.

ISBN: 9783031435836

Standard No.: 10.1007/978-3-031-43583-6doiSubjects--Topical Terms:

1090755
Neutrinos.

LC Class. No.: QC793.5.N42

Dewey Class. No.: 539.7215

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520 $a 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 studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.
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