Deep learning methods are becoming key in the data analysis of particle physics experiments. One clear example is the improvement of neutrino detection using neural networks. 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 the...
The IceCube neutrino observatory is a gigaton-scale water Cherenkov detector located at the South Pole instrumented with 5160 optical modules in a cubic kilometer of ice. When a high energy neutrino undergoes deep inelastic scattering, the inelasticity of the interaction is the fraction of energy deposited in the hadronic shower to the incoming neutrino energy. For a muon neutrino event, where...
Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. In this talk, I will present sparse submanifold convolutions (SSCNNs) as a solution to these issues and show that the...
The MINERvA experiment studies neutrinos cross sections with different nuclei. Neutrino vertex
recognition plays a key role in reconstructing neutrino interactions. This research aims to enhance
previous Machine Learning neutrino vertex recognition models produced in MINERvA using Deep
Convolutional Neural Networks (DCNN). The approach focuses on extending neutrino interaction
image...
