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Found 4 papers, 0 papers with code
Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers
The images generated by these detectors are extremely sparse, as the energy values detected by most of the detector are equal to 0, meaning that despite their high resolution, most of the detector is unused in a particular interaction.
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.
