Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA

The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4$$\sigma $$ σ if the true ordering is normal and 2.3$$\sigma $$ σ if inverted, after 3 years of data taking. The precision to measure $$\varDelta m^2_{32}$$ Δ m 32 2 and $$\theta _{23}$$ θ 23 were also estimated and found to be $$85 . 10^{-6}~{\mathrm{eV}^{2}}$$ 85 . 10 - 6 eV 2 and $$(^{+1.9}_{-3.1})^{\circ }$$ ( - 3.1 + 1.9 ) ∘ for normal neutrino mass ordering and, $$75 . 10^{-6}~{\mathrm{eV}^{2}}$$ 75 . 10 - 6 eV 2 and $$(^{+2.0}_{-7.0})^{\circ }$$ ( - 7.0 + 2.0 ) ∘ for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude "Equation missing" event rate variations larger than 20% at $$3\sigma $$ 3 σ level.

CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors

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.

Neutrino mass ordering determination through combined analysis with JUNO and KM3NeT/ORCA

The neutrino mass ordering (NMO) is one of the fundamental questions in neutrino physics. KM3NeT/ORCA and JUNO are two neutrino oscillation experiments both aiming at measuring the NMO with different approaches: ORCA with atmospheric neutrinos traversing the Earth and JUNO with reactor neutrinos. This contribution presents the potential of determining the NMO through a combined analysis of JUNO and ORCA data. In a joint fit, the NMO sensitivity is enhanced beyond the simple sum of the sensitivities of each experiment due to the tension between the respective Δm 31 2 best fit values obtained when the wrong ordering is assumed, together with good constraints on this parameter measurement by both experiments. From this analysis, we expect the true NMO to be determined with 5σ significance after 1–2 years of data taking by both experiments for the current global best-fit values of the oscillation parameters, while maximally 6 years will be needed for any other parameter set.

Analysis of the first KM3NeT-ORCA data

ORCA, Oscillation Research with Cosmics in the Abyss, is the low energy KM3NeT neutrino underwater detector, located in the French Mediterranean Sea. It comprises a dense array of optical modules designed to detect Cherenkov light emitted from charged particles resulting from neutrino interactions in the vicinity of the detector. Its main physics goal is the determination of the neutrino mass hierarchy by quantifying the matter-induced effect on the oscillation probabilities of atmospheric neutrinos in the energy range, 3–50 GeV, where the effects of neutrino oscillation phenomena are dominant. In 2019, four detection units were operational. Two more had been added in early 2020. This work presents an overview of the detector performance in the 2019 configuration, as well as its sensitivity to neutrino oscillations.

Atmospheric neutrinos with the first detection units of KM3NeT/ARCA

The KM3NeT collaboration is constructing two deep-sea Cherenkov detectors in the Mediterranean Sea. The ARCA detector aims at TeV-PeV neutrino astronomy, while the ORCA detector is optimised for atmospheric neutrino oscillation studies at energies of a few GeV. In this contribution, an analysis of the data collected with the first deployed detection units of the ARCA detector is presented. A high-purity sample of atmospheric neutrinos is selected demonstrating the capability of the ARCA detector.

Measuring muon tracks in Baikal-GVD using a fast reconstruction algorithm

The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$$^3$$ 3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of $$\sim $$ ∼ 100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast $$\chi ^2$$ χ 2 -based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks.

JUNO sensitivity to low energy atmospheric neutrino spectra

Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $$\nu _e$$ ν e and $$\nu _\mu $$ ν μ fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then processed by the detector simulation. The excellent timing resolution of the 3” PMT light detection system of JUNO detector and the much higher light yield for scintillation over Cherenkov allow to measure the time structure of the scintillation light with very high precision. Since $$\nu _e$$ ν e and $$\nu _\mu $$ ν μ interactions produce a slightly different light pattern, the different time evolution of light allows to discriminate the flavor of primary neutrinos. A probabilistic unfolding method has been used, in order to infer the primary neutrino energy spectrum from the detector experimental observables. The simulated spectrum has been reconstructed between 100 MeV and 10 GeV, showing a great potential of the detector in the atmospheric low energy region.

Validating the Earth’s core using atmospheric neutrinos with ICAL at INO

The Iron Calorimeter (ICAL) detector at the proposed India-based Neutrino Observatory (INO) aims to detect atmospheric neutrinos and antineutrinos separately in the multi-GeV range of energies and over a wide range of baselines. By utilizing its charge identification capability, ICAL can efficiently distinguish μ− and μ+ events. Atmospheric neutrinos passing long distances through Earth can be detected at ICAL with good resolution in energy and direction, which enables ICAL to see the density-dependent matter oscillations experienced by upward-going neutrinos in the multi-GeV range of energies. In this work, we explore the possibility of utilizing neutrino oscillations in the presence of matter to extract information about the internal structure of Earth complementary to seismic studies. Using good directional resolution, ICAL would be able to observe 331 μ− and 146 μ+ core-passing events with 500 kt·yr exposure. With this exposure, we show for the first time that the presence of Earth’s core can be independently confirmed at ICAL with a median ∆χ2 of 7.45 (4.83) assuming normal (inverted) mass ordering by ruling out the simple two-layered mantle-crust profile in theory while generating the prospective data with the PREM profile. We observe that in the absence of charge identification capability of ICAL, this sensitivity deteriorates significantly to 3.76 (1.59) for normal (inverted) mass ordering.