Search Results for author: Andreas Søgaard

Found 3 papers, 2 papers with code

GraphNeT: Graph neural networks for neutrino telescope event reconstruction

1 code implementation21 Oct 2022 Andreas Søgaard, Rasmus F. Ørsøe, Leon Bozianu, Morten Holm, Kaare Endrup Iversen, Tim Guggenmos, Martin Ha Minh, Philipp Eller, Troels C. Petersen

GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs).

Learning optimal wavelet bases using a neural network approach

1 code implementation25 Mar 2017 Andreas Søgaard

A novel method for learning optimal, orthonormal wavelet bases for representing 1- and 2D signals, based on parallels between the wavelet transform and fully connected artificial neural networks, is described.

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

no code implementations10 Mar 2017 Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.

Jet Tagging

Cannot find the paper you are looking for? You can Submit a new open access paper.