New Flux Limits in the Low Relativistic Regime for Magnetic Monopoles at IceCube

Magnetic monopoles are hypothetical particles that carry magnetic charge. Depending on their velocity, different light production mechanisms exist to facilitate detection. In this work, a previously unused light production mechanism, luminescence of ice, is introduced. This light production mechanism is nearly independent of the velocity of the incident magnetic monopole and becomes the only viable light production mechanism in the low relativistic regime (0.1-0.55c). An analysis in the low relativistic regime searching for magnetic monopoles in seven years of IceCube data is presented. While no magnetic monopole detection can be claimed, a new flux limit in the low relativistic regime is presented, superseding the previous best flux limit by 2 orders of magnitude

All-flavor constraints on nonstandard neutrino interactions and generalized matter potential with three years of IceCube DeepCore data

We report constraints on nonstandard neutrino interactions (NSI) from the observation of atmospheric neutrinos with IceCube, limiting all individual coupling strengths from a single dataset. Furthermore, IceCube is the first experiment to constrain flavor-violating and nonuniversal couplings simultaneously. Hypothetical NSI are generically expected to arise due to the exchange of a new heavy mediator particle. Neutrinos propagating in matter scatter off fermions in the forward direction with negligible momentum transfer. Hence the study of the matter effect on neutrinos propagating in the Earth is sensitive to NSI independently of the energy scale of new physics. We present constraints on NSI obtained with an all-flavor event sample of atmospheric neutrinos based on three years of IceCube DeepCore data. The analysis uses neutrinos arriving from all directions, with reconstructed energies between 5.6 GeV and 100 GeV. We report constraints on the individual NSI coupling strengths considered singly, allowing for complex phases in the case of flavor-violating couplings. This demonstrates that IceCube is sensitive to the full NSI flavor structure at a level competitive with limits from the global analysis of all other experiments. In addition, we investigate a generalized matter potential, whose overall scale and flavor structure are also constrained

A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors

IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment's photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies $>1$ TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to $20\%$ for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors

POCAM in the IceCube Upgrade

The IceCube Neutrino Observatory at the geographic South Pole instruments a gigaton of glacial Antarctic ice with over 5000 photosensors. The detector, by now running for over a decade, will be upgraded with seven new densely instrumented strings. The project focuses on the improvement of low-energy and oscillation physics sensitivities as well as re-calibration of the existing detector. Over the last few years we developed a Precision Optical Calibration Module (POCAM) providing self-monitored, isotropic, nanosecond, light pulses for optical calibration of large-volume detectors. Over 20 next-generation POCAMs will be calibrated and deployed in the IceCube Upgrade in order to reduce existing detector systematics. We report a general overview of the POCAM instrument, its performance and calibration procedures

A search for time-dependent astrophysical neutrino emission with IceCube data from 2012 to 2017

High-energy neutrinos are unique messengers of the high-energy universe, tracing the processes of cosmic-ray acceleration. This paper presents analyses focusing on time-dependent neutrino point-source searches. A scan of the whole sky, making no prior assumption about source candidates, is performed, looking for a space and time clustering of high-energy neutrinos in data collected by the IceCube Neutrino Observatory between 2012 and 2017. No statistically significant evidence for a time-dependent neutrino signal is found with this search during this period since all results are consistent with the background expectation. Within this study period, the blazar 3C 279, showed strong variability, inducing a very prominent gamma-ray flare observed in 2015 June. This event motivated a dedicated study of the blazar, which consists of searching for a time-dependent neutrino signal correlated with the gamma-ray emission. No evidence for a time-dependent signal is found. Hence, an upper limit on the neutrino fluence is derived, allowing us to constrain a hadronic emission model

Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube

A Search for Neutrinos from Decaying Dark Matter in Galaxy Clusters and Galaxies with IceCube

The observed dark matter abundance in the Universe can be explained with non-thermal, heavy dark matter models. In order for dark matter to still be present today, its lifetime has to far exceed the age of the Universe. In these scenarios, dark matter decay can produce highly energetic neutrinos, along with other Standard Model particles. To date, the IceCube Neutrino Observatory is the world’s largest neutrino telescope, located at the geographic South Pole. In 2013, the IceCube collaboration reported the first observation of high-energy astrophysical neutrinos. Since then, IceCube has collected a large amount of astrophysical neutrino data with energies up to tens of PeV, allowing us to probe the heavy dark matter models using neutrinos. We search the IceCube data for neutrinos from decaying dark matter in galaxy clusters and galaxies. The targeted dark matter masses range from 10 TeV to 10 PeV. In this contribution, we present the method and sensitivities of the analysis

A Combined Fit of the Diffuse Neutrino Spectrum using IceCube Muon Tracks and Cascades

The IceCube Neutrino Observatory first observed a diffuse flux of high energy astrophysical neutrinos in 2013. Since then, this observation has been confirmed in multiple detection channels such as high energy starting events, cascades, and through-going muon tracks. Combining these event selections into a high statistics global fit of 10 years of IceCube’s neutrino data could strongly improve the understanding of the diffuse astrophysical neutrino flux: challenging or confirming the simple unbroken power-law flux model as well as the astrophysical neutrino flux composition. One key component of such a combined analysis is the consistent modelling of systematic uncertainties of different event selections. This can be achieved using the novel SnowStorm Monte Carlo method which allows constraints to be placed on multiple systematic parameters from a single simulation set. We will report on the status of a new combined analysis of through-going muon tracks and cascades. It is based on a consistent all flavor neutrino signal and background simulation using, for the first time, the SnowStorm method to analyze IceCube’s high-energy neutrino data. Estimated sensitivities for the energy spectrum of the diffuse astrophysical neutrino flux will be shown

Measurement of Astrophysical Tau Neutrinos in IceCube's High-Energy Starting Events

We present the results of a search for astrophysical tau neutrinos in 7.5 years of IceCube's high-energy starting event data. At high energies, two energy depositions stemming from the tau neutrino charged-current interaction and subsequent tau lepton decay may be resolved. We report the first detection of two such events, with probabilities of $\sim 76\%$ and $\sim 98\%$ of being produced by astrophysical tau neutrinos. The resultant astrophysical neutrino flavor measurement is consistent with expectations, disfavoring a no-astrophysical tau neutrino flux scenario with 2.8$\sigma$ significance.Comment: This article is supported by a long-form paper that discusses the high-energy starting event selection titled: "The IceCube high-energy starting event sample: Description and flux characterization with 7.5 years of data.