| Abstract: |
Deep learning methods are becoming key in analysing some elementary particles of the Standard Model: the neutrinos. Current neutrino experiments are leveraging these techniques, which in combination have exhibited to outperform standard tools in several domains, such as identifying neutrino interactions or reconstructing particle energies. In this seminar, I will show various deep-learning algorithms used in the context of the T2K and DUNE experiments, two cutting-edge international neutrino experiments. I will present how computer vision and methods originally designed for natural language processing can be used for reconstruction tasks, including particle fitting and particle identification. All these methods report promising results and are already integrated (or planned to be) into the reconstruction chain of the experiments. |
