Name: Domain Generalization with Machine Learning in the Nova Experiment (en Inglés)
Price: 749.14 PEN
Availability: InStock
Author: Sutton, Andrew T. C.
ISBN: 9783031435829

portada Domain Generalization with Machine Learning in the Nova Experiment (en Inglés)

Formato

Libro Físico

Autor

Andrew T. C. Sutton

Editorial

Springer

Idioma

Inglés

N° páginas

170

Encuadernación

Tapa Dura

Dimensiones

23.4 x 15.6 x 1.3 cm

Peso

0.44 kg.

ISBN13

9783031435829

Categorías

Física nuclear
Instrumentos e instrumentación
Inteligencia artificial

Domain Generalization with Machine Learning in the Nova Experiment (en Inglés)

Andrew T. C. Sutton (Autor) · Springer · Tapa Dura

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S/ 749,14

S/ 1.498,27

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Reseña del libro "Domain Generalization with Machine Learning in the Nova Experiment (en Inglés)"

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 offalsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.