Related papers: Graph Neural Networks for Low-Energy Event Classif…
Effective channel estimation CE is critical for optimizing the performance of 5G New Radio NR systems particularly in dynamic environments where traditional methods struggle with complexity and adaptability This paper introduces GraphNet a…
A novel algorithm to reconstruct neutrino-induced particle showers within the ANTARES neutrino telescope is presented. The method achieves a median angular resolution of $6^\circ$ for shower energies below 100 TeV. Applying this algorithm…
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while…
We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for analyzing traffic data, demonstrating its efficacy in two critical tasks: forecasting and reconstruction. Unlike typical GNN methods that treat each traffic sensor…
The IceCube Neutrino Observatory actively participates in multimessenger follow-ups of gravitational-wave (GW) events. With the release of the Gravitational-Wave Transient Catalogue (GWTC)-2.1 and -3, the sub-threshold GW event information…
The IceCube Neutrino Observatory, located at the geographic South Pole, uses the glacial ice volume to detect astrophysical neutrinos. Detection of the neutrinos from the northern sky provides the opportunity to use a large effective…
The IceCube realtime alert system has been operating since 2016. It provides prompt alerts on high-energy neutrino events to the astroparticle physics community. The localization regions for the incoming direction of neutrinos are published…
Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising approach that can improve…
We analyze the performance of graph neural network (GNN) architectures from the perspective of random graph theory. Our approach promises to complement existing lenses on GNN analysis, such as combinatorial expressive power and worst-case…
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks,…
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is typically composed of a $K$-nearest neighbor (KNN) search and a…
The combined information from cosmic ray air showers that trigger both the surface and underground parts of the IceCube Neutrino Observatory allows the reconstruction of both the energy and mass of the primary particle through the knee…
Accurate measurement of neutrino energies is essential to many of the scientific goals of large-volume neutrino telescopes. The fundamental observable in such detectors is the Cherenkov light produced by the transit through a medium of…
The IceCube Neutrino Observatory is a cubic kilometer neutrino telescope located at the geographic South Pole. Cherenkov radiation emitted by charged secondary particles from neutrino interactions is observed by IceCube using an array of…
The IceCube telescope has detected diffuse neutrino emission, 20 events of which were reported to be above 60~TeV. In this paper, we fit the diffuse neutrino spectrum using Poisson statistics, which are the most appropriate for the low…
Although they are best known for studying astrophysical neutrinos, neutrino telescopes like IceCube can study neutrino interactions, at energies far above those that are accessible at accelerators. In this writeup, I present two IceCube…
The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $\sin^2 2\theta_{12}$, $\Delta…
Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data, their…
Observation of a point source of astrophysical neutrinos would be a "smoking gun" signature of a cosmic-ray accelerator. While IceCube has recently discovered a diffuse flux of astrophysical neutrinos, no localized point source has been…
Recently, the IceCube Neutrino Observatory has reported a deviation from the single power law in the extragalactic diffuse neutrino flux. A neural network-based event selection of contained and uncontained cascade events from IceCube, in…