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Found 4 papers, 1 papers with code
CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation
Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC).
Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors
Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and next-generation neutrino oscillation experiments.
Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC).
Jet Constituents for Deep Neural Network Based Top Quark Tagging
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables.
