Speaker
Dr
Saul Alonso Monsalve
(ETH Zurich)
Description
Deep learning is playing an increasingly important role in particle physics, offering powerful tools to tackle complex challenges in data analysis. This talk presents a range of advanced deep-learning techniques applied to neutrino physics, with a particular focus on the T2K experiment. The discussion includes the use of cutting-edge models such as transformers or sparse submanifold convolutional nets. These approaches have been employed to improve neutrino interaction identification and enhance the reconstruction of particle kinematics. By integrating these techniques, we aim to refine data analysis pipelines, boost measurement precision, and gain deeper insights into neutrino properties.
| AI keywords | transformers; domain adaptation; anomaly detection |
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Author
Dr
Saul Alonso Monsalve
(ETH Zurich)
