Reconstructing Neutrino Energy using CNNs for GeV Scale IceCube Events
Proceedings of Science 395 (2022)
Abstract:
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.Reconstruction of stereoscopic CTA events using deep learning with CTLearn
Proceedings of Science 395 (2022)
Abstract:
The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input.Search for Astrophysical Neutrino Transients with IceCube DeepCore
Proceedings of Science 395 (2022)
Abstract:
DeepCore, as a densely instrumented sub-detector of IceCube, extends IceCube’s energy reach down to about 10 GeV, enabling the search for astrophysical transient sources, e.g., choked gamma-ray bursts. While many other past and on-going studies focus on triggered time-dependent analyses, we aim to utilize a newly developed event selection and dataset for an untriggered all-sky time-dependent search for transients. In this work, all-flavor neutrinos are used, where neutrino types are determined based on the topology of the events. We extend the previous DeepCore transient half-sky search to an all-sky search and focus only on short timescale sources (with a duration of 102 ∼ 105 seconds). All-sky sensitivities to transients in an energy range from 10 GeV to 300 GeV will be presented in this poster. We show that DeepCore can be reliably used for all-sky searches for short-lived astrophysical sources.Searching for High-Energy Neutrinos from Core-Collapse Supernovae with IceCube
Proceedings of Science 395 (2022)
Abstract:
IceCube is a cubic kilometer neutrino detector array in the Antarctic ice that was designed to search for astrophysical, high-energy neutrinos. It has detected a diffuse flux of astrophysical neutrinos that appears to be of extragalactic origin. A possible contribution to this diffuse flux could stem from core-collapse supernovae. The high-energy neutrinos could either come from the interaction of the ejecta with a dense circumstellar medium or a jet, emanating from the star’s core, that stalls in the star’s envelope. Here, we will present results of a stacking analysis to search for this high-energy neutrino emission from core-collapse supernovae using 7 years of υμ track events from IceCube.Searching for neutrino transients below 1 TeV with IceCube
Proceedings of Science 395 (2022)