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Found 3 papers, 0 papers with code
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers
TransformerCVN classifies events with 90\% accuracy and improves the reconstruction of individual particles by 6\% over baseline methods which lack the integrated architecture of TransformerCVN.
Deep-Learning-Based Kinematic Reconstruction for DUNE
To precisely reconstruct these kinematic characteristics of detected interactions at DUNE, we have developed and will present two CNN-based methods, 2-D and 3-D, for the reconstruction of final state particle direction and energy, as well as neutrino energy.
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference
The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics.
