Conveners
Day 2 Morning
- Kazuhiro Terao (SLAC)
- Wouter Van De Pontseele (Harvard University)
Graph neural networks (GNNs) are a category of neural networks which operate on graph-structured inputs, instead of the grid-structured inputs required by a CNN. Building on work developed for the HL-LHC for particle tracking with GNNs as part of the Exa.TrkX collaboration, this talk presents work to develop GNN-based techniques for hit-level reconstruction in Liquid Argon Time Projection...
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. Some neutrino experiments are already using them, for example, for distinguishing among different neutrino topologies.
In this talk, we report on the performance of a graph neural network (GNN) approach in assisting with...
Normalizing flows present a powerful framework to sample and evaluate probability density functions via neural networks. In this work we address two applications of them in the domain of neutrino physics: i) Perform likelihood-free inference of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed...
PROSPECT is an antineutrino detector located above ground at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The energy spectrum of antineutrinos emitted from the reactors is measured by using a delayed coincidence technique through the inverse-beta-decay reaction (IBD). The ORNL group is currently exploring several applications of machine learning techniques for...
