Article

Characterization of the astrophysical diffuse neutrino flux using starting track events in IceCube

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Abstract

A measurement of the diffuse astrophysical neutrino spectrum is presented using IceCube data collected from 2011–2022 (10.3 years). We developed novel detection techniques to search for events with a contained vertex and exiting track induced by muon neutrinos undergoing a charged-current interaction. Searching for these starting track events allows us to not only more effectively reject atmospheric muons but also atmospheric neutrino backgrounds in the southern sky, opening a new window to the sub-100 TeV astrophysical neutrino sky. The event selection is constructed using a dynamic starting track veto and machine learning algorithms. We use this data to measure the astrophysical diffuse flux as a single power law flux (SPL) with a best-fit spectral index of γ=2.58−0.09+0.10 and per-flavor normalization of ϕper−flavorAstro=1.68−0.22+0.19×10−18×GeV−1 cm−2 s−1 sr−1 (at 100 TeV). The sensitive energy range for this dataset is 3–550 TeV under the SPL assumption. This data was also used to measure the flux under a broken power law, however we did not find any evidence of a low energy cutoff.

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A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03450-1.
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We report on the first measurement of the astrophysical neutrino flux using particle showers (cascades) in IceCube data from 2010–2015. Assuming standard oscillations, the astrophysical neutrinos in this dedicated cascade sample are dominated (∼90%) by electron and tau flavors. The flux, observed in the sensitive energy range from 16 TeV to 2.6 PeV, is consistent with a single power-law model as expected from Fermi-type acceleration of high energy particles at astrophysical sources. We find the flux spectral index to be γ=2.53±0.07 and a flux normalization for each neutrino flavor of ϕastro=1.66−0.27+0.25 at E0=100 TeV, in agreement with IceCube’s complementary muon neutrino results and with all-neutrino flavor fit results. In the measured energy range we reject spectral indices γ≤2.28 at ≥3σ significance level. Because of high neutrino energy resolution and low atmospheric neutrino backgrounds, this analysis provides the most detailed characterization of the neutrino flux at energies below ∼100 TeV compared to previous IceCube results. Results from fits assuming more complex neutrino flux models suggest a flux softening at high energies and a flux hardening at low energies (p value ≥0.06). The sizable and smooth flux measured below ∼100 TeV remains a puzzle. In order to not violate the isotropic diffuse gamma-ray background as measured by the Fermi Large Area Telescope, it suggests the existence of astrophysical neutrino sources characterized by dense environments which are opaque to gamma rays.
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Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
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We consider limits on the local ($z=0$) density ($n_0$) of extragalactic neutrino sources set by the nondetection of steady high-energy neutrino sources producing $\gtrsim30$ TeV muon multiplets in the present IceCube data, taking into account the redshift evolution, luminosity function and neutrino spectrum of the sources. We show that the lower limit depends weakly on source spectra and strongly on redshift evolution. We find $n_0\gtrsim{10}^{-7}~{\rm Mpc}^{-3}$ for standard candle sources evolving rapidly, $n_s\propto{(1+z)}^3$, and $n_0\gtrsim{10}^{-5}~{\rm Mpc}^{-3}$ for nonevolving sources. The corresponding upper limits on their neutrino luminosity are $L_{{\nu_\mu}}^{\rm eff}\lesssim10^{42}~{\rm erg}~{\rm s}^{-1}$ and $L_{{\nu_\mu}}^{\rm eff}\lesssim10^{41}~{\rm erg}~{\rm s}^{-1}$, respectively. Applying these results to a wide range of classes of potential sources, we show that powerful blazar jets associated with active galactic nuclei are unlikely to be the dominant sources. For almost all other steady candidate source classes (including starbursts, radio galaxies, and galaxy clusters and groups), an order of magnitude increase in the detector sensitivity at $\sim0.1-1$ PeV will enable a detection (as point sources) of the few brightest objects. Such an increase, which may be provided by next-generation detectors like IceCube-Gen2 and an upgraded KM3NET, can improve the limit on $n_0$ by more than two orders of magnitude. Future gamma-ray observations (by Fermi, HAWC and CTA) will play a key role in confirming the association of the neutrinos with their sources.
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The IceCube neutrino observatory in operation at the South Pole, Antarctica, comprises three distinct components: a large buried array for ultrahigh energy neutrino detection, a surface air shower array, and a new buried component called DeepCore. DeepCore was designed to lower the IceCube neutrino energy threshold by over an order of magnitude, to energies as low as about 10 GeV. DeepCore is situated primarily 2100 m below the surface of the icecap at the South Pole, at the bottom center of the existing IceCube array, and began taking physics data in May 2010. Its location takes advantage of the exceptionally clear ice at those depths and allows it to use the surrounding IceCube detector as a highly efficient active veto against the principal background of downward-going muons produced in cosmic-ray air showers. DeepCore has a module density roughly five times higher than that of the standard IceCube array, and uses photomultiplier tubes with a new photocathode featuring a quantum efficiency about 35% higher than standard IceCube PMTs. Taken together, these features of DeepCore will increase IceCube's sensitivity to neutrinos from WIMP dark matter annihilations, atmospheric neutrino oscillations, galactic supernova neutrinos, and point sources of neutrinos in the northern and southern skies. In this paper we describe the design and initial performance of DeepCore. (c) 2012 Elsevier B.V. All rights reserved.
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The hypothesis of an unstable charged boson to mediate muon decay radically affects the cross section for the process $\overline{$\nu${}}+e$\rightarrow${}\overline{$\nu${}}+{$\mu${}}^{$-${}}$ near the energy at which the intermediary may be produced. If the boson is assumed to have $K$-meson mass, the resonance occurs at an incident antineutrino energy of $\sim${}2\ifmmode\times\else\texttimes\fi{}${10}^{12}$ ev. The flux of energetic antineutrinos produced in association with cosmic-ray muons will then produce two muon counts per day per square meter of detector, independently of the depth and the orientation at which the experiment is performed.
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The Fisher information matrix summarizes the amount of information in the data relative to the quantities of interest. There are many applications of the information matrix in modeling, systems analysis, and estimation, including confidence region calculation, input design, prediction bounds, and “noninformative” priors for Bayesian analysis. This article reviews some basic principles associated with the information matrix, presents a resampling-based method for computing the information matrix together with some new theory related to efficient implementation, and presents some numerical results. The resampling-based method relies on an efficient technique for estimating the Hessian matrix, introduced as part of the adaptive (“second-order”) form of the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.
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Neutrino telescopes such as IceCube search for an excess of high energy neutrinos above the steeply falling atmospheric background as one approach to finding extraterrestrial neutrinos. For samples of events selected to start in the detector, the atmospheric background can be reduced to the extent that a neutrino interaction inside the fiducial volume is accompanied by a detectable muon from the same cosmic-ray cascade in which the neutrino was produced. Here we provide an approximate calculation of the veto probability as a function of neutrino energy and zenith angle.
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The IceCube project has transformed 1 km3 of deep natural Antarctic ice into a Cherenkov detector. Muon neutrinos are detected and their direction is inferred by mapping the light produced by the secondary muon track inside the volume instrumented with photomultipliers. Reconstructing the muon track from the observed light is challenging due to noise, light scattering in the ice medium, and the possibility of simultaneously having multiple muons inside the detector, resulting from the large flux of cosmic ray muons. This paper describes work on two problems: (1) the track reconstruction problem, in which, given a set of observations, the goal is to recover the track of a muon; and (2) the coincident event problem, which is to determine how many muons are active in the detector during a time window. Rather than solving these problems by developing more complex physical models that are applied at later stages of the analysis, our approach is to augment the detector's early reconstruction with data filters and robust statistical techniques. These can be implemented at the level of on-line reconstruction and, therefore, improve all subsequent reconstructions. Using the metric of median angular resolution, a standard metric for track reconstruction, we improve the accuracy in the initial reconstruction direction by 13%. We also present improvements in measuring the number of muons in coincident events: we can accurately determine the number of muons 98% of the time.
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Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.