Neutrinos are neutral, fundamental particles that oscillate, or change flavor, while they travel. This phenomenon means that neutrinos deviate from the Standard Model prediction that they are ...massless, creating an exciting window into physics exploration beyond the Standard Model. Understanding neutrino oscillation and constraining their behavior is crucial to furthering the understanding of these particles and how they fit, or do not fit, into the Standard Model. The IceCube Neutrino Observatory has been detecting neutrinos for more than 10 years, leading to a large sample of atmospheric neutrino data available for studying neutrino oscillations. Reconstructing the neutrino interactions, such as the neutrino’s energy and direction, are key to constraining neutrino oscillation. A fast and robust reconstruction method was developed using convolutional neural networks (CNNs) and optimized to reconstruct parameters necessary to both reconstruct and isolate a pure atmospheric neutrino sample using the IceCube detector. This work compares the performance of this reconstruction to the current likelihood-based reconstruction currently used in IceCube. An analysis of the muon neutrino disappearance is then pursued using 9.28 years of neutrino data. The analysis shows competitive projected sensitivity, the ability to account for numerous systematics, and robust recovery of the physics parameters under statistical and systematic variations. While the 9.28 year sample is still under collaboration review, one year of the total data is used to perform a confirmatory study on the CNN reconstruction and sample. The oscillation parameter constraints from this one year analysis are in alignment with past IceCube analyses and other neutrino experiments within one sigma. This opens the pathway to use the CNN reconstruction for future analyses studying low energy neutrinos on IceCube.
The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube's primary scientific goal is the detection of TeV neutrino ...emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample is presented and compared to the existing worldwide measurements.
Measurements of neutrinos at and below 10 GeV provide unique constraints of neutrino oscillation parameters as well as probes of potential Non-Standard Interactions (NSI). The IceCube Neutrino ...Observatory's DeepCore array is designed to detect neutrinos down to GeV energies. IceCube has built the world's largest data set of neutrinos >10 GeV, making searches for NSI a computational challenge. This work describes the use of convolutional neural networks (CNNs) to improve the energy reconstruction resolution and speed of reconstructing O(10 GeV) neutrino events in IceCube. Compared to current likelihood-based methods which take seconds to minutes, the CNN is expected to provide approximately a factor of 2 improvement in energy resolution while reducing the reconstruction time per event to milliseconds, which is essential for processing large datasets.
The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice, instruments a cubic kilometer of ice with 5,160 optical modules that detect Cherenkov radiation originating from neutrino ...interactions. The more densely instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-GeV scales to improve important measurements of fundamental neutrino properties such as the oscillation parameters and to search for non-standard interactions. Sensitivity to oscillation parameters, dependent on the distance traveled over the neutrino energy (L/E), is limited in IceCube by the resolution of the arrival angle (which determines L) and energy (E). Event reconstruction improvements can therefore directly lead to advancements in oscillation results. This work uses a Convolutional Neural Network (CNN) to reconstruct the energy of 10-GeV scale neutrino events in IceCube, providing results with competitive resolutions and faster runtimes than previous likelihood-based methods.
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