Search for sub-TeV Neutrino Emission from Novae with IceCube-DeepCore

The understanding of novae, the thermonuclear eruptions on the surfaces of white dwarf stars in binaries, has recently undergone a major paradigm shift. Though the bolometric luminosity of novae was long thought to arise directly from photons supplied by the thermonuclear runaway, recent gigaelectronvolt (GeV) gamma-ray observations have supported the notion that a significant portion of the luminosity could come from radiative shocks. More recently, observations of novae have lent evidence that these shocks are acceleration sites for hadrons for at least some types of novae. In this scenario, a flux of neutrinos may accompany the observed gamma rays. As the gamma rays from most novae have only been observed up to a few GeV, novae have previously not been considered as targets for neutrino telescopes, which are most sensitive at and above teraelectronvolt (TeV) energies. Here, we present the first search for neutrinos from novae with energies between a few GeV and 10 TeV using IceCube-DeepCore, a densely instrumented region of the IceCube Neutrino Observatory with a reduced energy threshold. We search both for a correlation between gamma-ray and neutrino emission as well as between optical and neutrino emission from novae. We find no evidence for neutrino emission from the novae considered in this analysis and set upper limits for all gamma-ray detected novae.R. Abbasi ... R. T. Burley ... E. G. Carnie-Bronca ... G. H. Collin ... G. C. Hill ... E. J. Roberts ... et al. (IceCube Collaboration

Search for Correlations of High-energy Neutrinos Detected in IceCube with Radio-bright AGN and Gamma-Ray Emission from Blazars

Published 2023 August 23The IceCube Neutrino Observatory sends realtime neutrino alerts with a high probability of being astrophysical in origin. We present a new method to correlate these events and possible candidate sources using 2089 blazars from the Fermi-LAT 4LAC-DR2 catalog and with 3413 active galactic nuclei (AGNs) from the Radio Fundamental Catalog. No statistically significant neutrino emission was found in any of the catalog searches. The result suggests that a small fraction, <1%, of the studied AGNs emit neutrinos that pass the alert criteria, and is compatible with prior evidence for neutrino emission presented by IceCube and other authors from sources such as TXS 0506 + 056 and PKS 1502 + 106. We also present cross-checks to other analyses that claim a significant correlation using similar data samples.R. Abbasi ... R. T. Burley ... E. G. Carnie-Bronca ... G. H. Collin ... G. C. Hill ... E. J. Roberts ... et al. (IceCube Collaboration

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

Published: November 4, 2022IceCube, 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 GeV–100 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%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 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.R. Abbasi ... R.T. Burley ... E.G. Carnie-Bronca ... G.H. Collin ... G.C. Hill ... E.J. Roberts ... et al. (The IceCube collaboration

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 GeV–100 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%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 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