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Register a new userTagging Spallation Backgrounds with Showers in Water-Cherenkov Detectors
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Cosmic-ray muons and especially their secondaries break apart nuclei (spallation) and produce fast neutrons and beta-decay isotopes, which are backgrounds for low-energy experiments. In Super-Kamiokande, these beta decays are the dominant background in 6--18 MeV, relevant for solar neutrinos and the diffuse supernova neutrino background. In a previous paper, we showed that these spallation isotopes are produced primarily in showers, instead of in isolation. This explains an empirical spatial correlation between a peak in the muon Cherenkov light profile and the spallation decay, which Super-Kamiokande used to develop a new spallation cut. However, the muon light profiles that Super-Kamiokande measured are grossly inconsistent with shower physics. We show how to resolve this discrepancy and how to reconstruct accurate profiles of muons and their showers from their Cherenkov light. We propose a new spallation cut based on these improved profiles and quantify its effects. Our results can significantly benefit low-energy studies in Super-Kamiokande, and will be especially important for detectors at shallower depths, like the proposed Hyper-Kamiokande.
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Crucial questions about solar and supernova neutrinos remain unanswered. Super-Kamiokande has the exposure needed for progress, but detector backgrounds are a limiting factor. A leading component is the beta decays of isotopes produced by cosmic-ray muons and their secondaries, which initiate nuclear spallation reactions. Cuts of events after and surrounding muon tracks reduce this spallation decay background by $simeq 90%$ (at a cost of $simeq 20%$ deadtime), but its rate at 6--18 MeV is still dominant. A better way to cut this background was suggested in a Super-Kamiokande paper [Bays {it et al.}, Phys.~Rev.~D {bf 85}, 052007 (2012)] on a search for the diffuse supernova neutrino background. They found that spallation decays above 16 MeV were preceded near the same location by a peak in the apparent Cherenkov light profile from the muon; a more aggressive cut was applied to a limited section of the muon track, leading to decreased background without increased deadtime. We put their empirical discovery on a firm theoretical foundation. We show that almost all spallation decay isotopes are produced by muon-induced showers and that these showers are rare enough and energetic enough to be identifiable. This is the first such demonstration for any detector. We detail how the physics of showers explains the peak in the muon Cherenkov light profile and other Super-K observations. Our results provide a physical basis for practical improvements in background rejection that will benefit multiple studies. For solar neutrinos, in particular, it should be possible to dramatically reduce backgrounds at energies as low as 6 MeV.
Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and next-generation neutrino oscillation experiments. In this work, we demonstrate the capability of variational autoencoders and normalizing flows to approximate the generative distribution of simulated data for water Cherenkov detectors commonly used in these experiments. We study the performance of these methods and their applicability for semi-supervised learning and synthetic data generation.
Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product $^{10}$C is an important background in the region of interest between 2-3 MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network that uses the temporal and spatial correlations in light emissions to identify $^{10}$C background events. With a typical kiloton-scale detector configuration like the KamLAND detector, we find that the algorithm is capable of identifying 61.6% of the $^{10}$C at 90% signal acceptance. A detector with perfect light collection could identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.
Evidence of azimuthal asymmetries in the time structure and signal size has been found in non-vertical showers as a function of zenith angle. These asymmetries arise because of the different paths traveled by particles in the upper and lower sides of the plane perpendicular to the shower axis to reach detectors at the same axial distances. The shower particles are differentially attenuated as they traverse the atmosphere. Furthermore, most particles are not propagating strictly in the shower direction but are on average going away from the axis. This geometrical projection effect also contributes to the final asymmetry. These novel observations must be understood for parameterisation of the lateral distribution function. Additionally, the asymmetry in time distributions offers a new possibility for the determination of the mass composition because its magnitude is strongly dependent on the fraction of electromagnetic signal at the observation level. The asymmetries found in data collected from the Engineering Array of the Auger Observatory will be compared with Monte Carlo data.
Gadolinium-loading of large water Cherenkov detectors is a prime method for the detection of the Diffuse Supernova Neutrino Background (DSNB). While the enhanced neutron tagging capability greatly reduces single-event backgrounds, correlated events mimicking the IBD coincidence signature remain a potentially harmful background. Neutral-Current (NC) interactions of atmospheric neutrinos potentially dominate the DSNB signal especially in the low-energy range of the observation window that reaches from about 12 to 30 MeV. The present paper investigates a novel method for the discrimination of this background. Convolutional Neural Networks (CNNs) offer the possibility for a direct analysis and classification of the PMT hit patterns of the prompt events. Based on the events generated in a simplified SuperKamiokande-like detector setup, we find that a trained CNN can maintain a signal efficiency of 96 % while reducing the residual NC background to 2 % of the original rate. Comparing to recent predictions of the DSNB signal and measurements of the NC background levels in Super-Kamiokande, the corresponding signal-to-background ratio is about 4:1, providing excellent conditions for a DSNB discovery.
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