191 research outputs found
Graph Neural Networks for low-energy event classification & reconstruction in IceCube
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeVâ100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%â20% compared to current maximum likelihood techniques in the energy range of 1 GeVâ30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.Peer Reviewe
Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeVïżœ100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%ïżœ20% compared to current maximum likelihood techniques in the energy range of 1 GeVïżœ30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events
IceCube -- Neutrinos in Deep Ice The Top 3 Solutions from the Public Kaggle Competition
During the public Kaggle competition "IceCube -- Neutrinos in Deep Ice",
thousands of reconstruction algorithms were created and submitted, aiming to
estimate the direction of neutrino events recorded by the IceCube detector.
Here we describe in detail the three ultimate best, award-winning solutions.
The data handling, architecture, and training process of each of these machine
learning models is laid out, followed up by an in-depth comparison of the
performance on the kaggle datatset. We show that on cascade events in IceCube
above 10 TeV, the best kaggle solution is able to achieve an angular resolution
of better than 5 degrees, and for tracks correspondingly better than 0.5
degrees. These performance measures compare favourably to the current
state-of-the-art in the field
Rejecting noise in Baikal-GVD data with neural networks
Baikal-GVD is a large ( 1 km) underwater neutrino telescope
installed in the fresh waters of Lake Baikal. The deep lake water environment
is pervaded by background light, which produces detectable signals in the
Baikal-GVD photosensors. We introduce a neural network for an efficient
separation of these noise hits from the signal ones, stemming from the
propagation of relativistic particles through the detector. The neural network
has a U-net like architecture and employs temporal (causal) structure of
events. On Monte-Carlo simulated data, it reaches 99% signal purity (precision)
and 98% survival efficiency (recall). The benefits of using neural network for
data analysis are discussed, and other possible architectures of neural
networks, including graph based, are examined
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments
The success of Convolutional Neural Networks (CNNs) in image classification
has prompted efforts to study their use for classifying image data obtained in
Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D
and 3D image data from particle physics experiments to classify signal from
background.
In this work we present an extensive convolutional neural architecture
search, achieving high accuracy for signal/background discrimination for a HEP
classification use-case based on simulated data from the Ice Cube neutrino
observatory and an ATLAS-like detector. We demonstrate among other things that
we can achieve the same accuracy as complex ResNet architectures with CNNs with
less parameters, and present comparisons of computational requirements,
training and inference times.Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide,
Australi
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