Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hex...
Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-...
International audienceDeep convolutional neural networks (DCNs) are a promising machine learning tec...
The field of deep learning has become increasingly important for particle physics experiments, yield...
Continued improvements on existing reconstruction methods are vital to the success of high-energy ph...
Reliable and accurate reconstruction methods are vital to the success of high-energy physics experim...
The IceCube Neutrino Observatory observes neutrinos interacting deep within the South Pole ice. It c...
The IceCube Neutrino Observatory instruments a cubic kilometer of ice at the South Pole to detect at...
Measurements of neutrinos at and below 10 GeV provide unique constraints of neutrino oscillation par...
The IceCube Neutrino Observatory is a cubic-kilometer scale neutrino detector embedded in the Antarc...
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical ne...
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical ne...
The reconstruction of event-level information, such as the direction or energy of a neutrino interac...
The IceCube Neutrino Observatory consists of 5,160 digital optical modules, which are deployed over ...
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes (IACTs) situated at the IceCube...
Detection of few-GeV atmospheric neutrinos is made possible by the DeepCore infill array to the IceC...
Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-...
International audienceDeep convolutional neural networks (DCNs) are a promising machine learning tec...
The field of deep learning has become increasingly important for particle physics experiments, yield...
Continued improvements on existing reconstruction methods are vital to the success of high-energy ph...
Reliable and accurate reconstruction methods are vital to the success of high-energy physics experim...
The IceCube Neutrino Observatory observes neutrinos interacting deep within the South Pole ice. It c...
The IceCube Neutrino Observatory instruments a cubic kilometer of ice at the South Pole to detect at...
Measurements of neutrinos at and below 10 GeV provide unique constraints of neutrino oscillation par...
The IceCube Neutrino Observatory is a cubic-kilometer scale neutrino detector embedded in the Antarc...
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical ne...
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical ne...
The reconstruction of event-level information, such as the direction or energy of a neutrino interac...
The IceCube Neutrino Observatory consists of 5,160 digital optical modules, which are deployed over ...
The IceAct telescopes are prototype Imaging Air Cherenkov telescopes (IACTs) situated at the IceCube...
Detection of few-GeV atmospheric neutrinos is made possible by the DeepCore infill array to the IceC...
Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-...
International audienceDeep convolutional neural networks (DCNs) are a promising machine learning tec...
The field of deep learning has become increasingly important for particle physics experiments, yield...