50 research outputs found

    The Reach of INO for Atmospheric Neutrino Oscillation Parameters

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    The India-based Neutrino Observatory (INO) will host a 50 kt magnetized iron calorimeter (ICAL@INO) for the study of atmospheric neutrinos. Using the detector resolutions and efficiencies obtained by the INO collaboration from a full-detector GEANT4-based simulation, we determine the reach of this experiment for the measurement of the atmospheric neutrino mixing parameters (sin2θ23\sin^2 \theta_{23} and Δm322|\Delta m_{32}^2 |). We also explore the sensitivity of this experiment to the deviation of θ23\theta_{23} from maximal mixing, and its octant.Comment: 19 pages, 18 pdf figures, Uses pdflate

    Enhancing sensitivity to neutrino parameters at INO combining muon and hadron information

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    The proposed ICAL experiment at INO aims to identify the neutrino mass hierarchy from observations of atmospheric neutrinos, and help improve the precision on the atmospheric neutrino mixing parameters. While the design of ICAL is primarily optimized to measure muon momentum, it is also capable of measuring the hadron energy in each event. Although the hadron energy is measured with relatively lower resolution, it nevertheless contains crucial information on the event, which may be extracted when taken concomitant with the muon data. We demonstrate that by adding the hadron energy information to the muon energy and muon direction in each event, the sensitivity of ICAL to the neutrino parameters can be improved significantly. Using the realistic detector response for ICAL, we present its enhanced reach for determining the neutrino mass hierarchy, the atmospheric mass squared difference and the mixing angle theta23, including its octant. In particular, we show that the analysis that uses hadron energy information can distinguish the normal and inverted mass hierarchies with Deltachi^2 approx 9 with 10 years exposure at the 50 kt ICAL, which corresponds to about 40% improvement over the muon-only analysis.Comment: 25 pages, 26 pdf figures, 3 tables. Comments are welcome. One new table (Table 3). New references added. Some parts of the text rewritten to improve the discussion. Matches with published versio

    Can INO be Sensitive to Flavor-Dependent Long-Range Forces?

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    Flavor-dependent long-range leptonic forces mediated by the ultra-light and neutral bosons associated with gauged LeLμL_e-L_\mu or LeLτL_e-L_\tau symmetry constitute a minimal extension of the Standard Model. In presence of these new anomaly free abelian symmetries, the SM remains invariant and renormalizable, and can lead to interesting phenomenological consequences. For an example, the electrons inside the Sun can generate a flavor-dependent long-range potential at the Earth surface, which can enhance νμ\nu_\mu and νˉμ\bar\nu_\mu survival probabilities over a wide range of energies and baselines in atmospheric neutrino experiments. In this paper, we explore in detail the possible impacts of these long-range flavor-diagonal neutral current interactions due to LeLμL_e-L_\mu and LeLτL_e-L_\tau symmetries (one at-a-time) in the context of proposed 50 kt magnetized ICAL detector at INO. Combining the information on muon momentum and hadron energy on an event-by-event basis, ICAL can place stringent constraints on the effective gauge coupling αeμ/eτ<1.2×1053\alpha_{e\mu/e\tau}<1.2\times 10^{-53} (1.75×10531.75\times 10^{-53}) at 90%\% (3σ\sigma) C.L. with 500 kt\cdotyr exposure. The 90%\% C.L. limit on αeμ\alpha_{e\mu} (αeτ\alpha_{e\tau}) from ICAL is 46\sim 46 (53) times better than the existing bound from the Super-Kamiokande experiment.Comment: 26 pages, 30 pdf figures, 2 table

    Physics Potential of the ICAL detector at the India-based Neutrino Observatory (INO)

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    The upcoming 50 kt magnetized iron calorimeter (ICAL) detector at the India-based Neutrino Observatory (INO) is designed to study the atmospheric neutrinos and antineutrinos separately over a wide range of energies and path lengths. The primary focus of this experiment is to explore the Earth matter effects by observing the energy and zenith angle dependence of the atmospheric neutrinos in the multi-GeV range. This study will be crucial to address some of the outstanding issues in neutrino oscillation physics, including the fundamental issue of neutrino mass hierarchy. In this document, we present the physics potential of the detector as obtained from realistic detector simulations. We describe the simulation framework, the neutrino interactions in the detector, and the expected response of the detector to particles traversing it. The ICAL detector can determine the energy and direction of the muons to a high precision, and in addition, its sensitivity to multi-GeV hadrons increases its physics reach substantially. Its charge identification capability, and hence its ability to distinguish neutrinos from antineutrinos, makes it an efficient detector for determining the neutrino mass hierarchy. In this report, we outline the analyses carried out for the determination of neutrino mass hierarchy and precision measurements of atmospheric neutrino mixing parameters at ICAL, and give the expected physics reach of the detector with 10 years of runtime. We also explore the potential of ICAL for probing new physics scenarios like CPT violation and the presence of magnetic monopoles.Comment: 139 pages, Physics White Paper of the ICAL (INO) Collaboration, Contents identical with the version published in Pramana - J. Physic

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches
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