0000000000417672

AUTHOR

Nora Valtonen-mattila

showing 2 related works from this author

LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories

2021

We present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples.

Particle physicsPhysics::Instrumentation and DetectorsComputer scienceAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesGeneral Physics and AstronomyCHERENKOV LIGHT YIELDWeighting01 natural sciencesHigh Energy Physics - Experiment010305 fluids & plasmasStandard ModelHigh Energy Physics - Experiment (hep-ex)Neutrino interactionHigh Energy Physics - Phenomenology (hep-ph)0103 physical sciences010306 general physicsCherenkov radiationEvent generatorEvent generator; Neutrino generator; Neutrino interaction; Neutrino simulation; WeightingGenerator (computer programming)hep-exEvent (computing)ICEHigh Energy Physics::PhenomenologyDetectorhep-phComputational Physics (physics.comp-ph)Quantitative Biology::GenomicsHigh Energy Physics - Phenomenologyphysics.comp-phHardware and ArchitectureHigh Energy Physics::ExperimentNeutrino simulationNeutrino generatorEvent generatorNeutrinoPhysics - Computational PhysicsLeptonComputer Physics Communications
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A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

2021

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 …

FOS: Computer and information sciencesComputer Science - Machine LearningAstrophysics::High Energy Astrophysical Phenomenacs.LGData analysisFOS: Physical sciencesFitting methods01 natural sciencesConvolutional neural networkCalibration; Cluster finding; Data analysis; Fitting methods; Neutrino detectors; Pattern recognitionHigh Energy Physics - ExperimentIceCube Neutrino ObservatoryMachine Learning (cs.LG)High Energy Physics - Experiment (hep-ex)Pattern recognition0103 physical sciencesNeutrino detectors010303 astronomy & astrophysicsInstrumentationMathematical Physics010308 nuclear & particles physicsbusiness.industryhep-exDeep learningCluster findingDetectorNeutrino detectorComputer engineeringOrders of magnitude (time)13. Climate actionCascadeCalibrationPattern recognition (psychology)Artificial intelligencebusiness
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