Browsing by Subject "NOvA"

Now showing 1 - 9 of 9
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    Escaping Events at the NOvA Far Detector
    (2018-08) Bychkov, Vladimir
    The NuMI Off-axis νe Appearance (NOvA) experiment studies neutrino oscillations in the NuMI (Neutrinos at the Main Injector) beam. The goal of the experiment is to determine the mass hierarchy and measure δCP , however it is also sensitive to sin2 θ23, and ∆m232. The present work tries to improve experimental sensitivity to the oscillation parameters by considering neutrino events which are discarded by the previous analysis — escaping events. The biggest issues with the escaping sample are significant cosmic background and correct energy estimation. Both issues were addressed and inclusion of escaping sample to the final fit led to an effective increase of statistics by 4% with best-fit measurements of sin2 θ23 = 0.504 and ∆m232 = 2.45 × 10−3 eV2.
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    Improving Energy Estimation at NOvA with Recurrent Neural Networks
    (2021-05) Torbunov, Dmitrii
    In this thesis I discuss the application of Recurrent Neural Networks to the problem of neutrino energy reconstruction at the NOvA experiment. NOvA is a long-baseline accelerator based neutrino oscillation experiment that holds one of the leading measurements of the $\Delta m_{32}^2$ oscillation parameter. In order to make precise measurements of the neutrino oscillation parameters, NOvA needs a good neutrino energy estimation algorithm. A new energy estimation algorithm that is based on a recurrent neural network architecture has been developed for NOvA. The new energy estimator has 15% better energy reconstruction than the previous energy estimation algorithm, and it is 5 times less sensitive to the major systematic uncertainty at NOvA. Using the new energy estimator has the potential to significantly improve the precision of measurements of the neutrino oscillation parameters at NOvA.
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    Improving Physics Based Electron Neutrino Appearance Identification with a Long Short-Term Memory Network
    (2018-09) Vold, Andrew
    The NOνA experiment is a long baseline neutrino oscillation experiment with the objective to measure the oscillation probability of muon type neutrinos (νμ) into electron type neutrinos (νe). NOνA measures the interactions of neutrinos from the NuMI beam in two functionally identical liquid scintillator detectors. The far detector detects the appearance of electron neutrinos, leading to measurement of the oscillation parameters under study. Using an off axis beam with an 810 km baseline length, NOνA is sensitive to measuring the neutrino mass hierarchy, the CP violating parameter, and the octant of the mixing angle, θ23. The data presented in this thesis has been collected from October 2013, until May 2018. The first NOνA νe charged current identifier utilized an artificial neural network with the physical features of the highest energy reconstructed shower as inputs. The νe charged current identifier in this thesis utilizes a Long Short-Term Memory network with the physical features of every reconstructed shower in a particular interaction. In addition to the Long Short-Term Memory network, there are two Boosted Decision Trees to assist in event level selection. In the analysis of the data, 54 νe candidate events were detected with an expected background of 15 events. The results of this analysis prefer the normal mass hierarchy with maximal mixing and δCP = 1.92+0.08. Results π −1.19 from this analysis differ from the published NOνA analysis, due to the differences of electron identification techniques.
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    Improving The Simulation Of Muons In The Rock Around The NOvA Near Detector
    (2023-08) Parvez, Radwan
    This thesis investigates the viability of the PROPOSAL lepton propagation tool as an alternative to the commonly used GEANT4 simulation for muon propagation within the NOvA experiment. The main objectives of this research were twofold: first, to validate the accuracy of PROPOSAL data in simulating rock muons when compared to GEANT4 data; second, to explore the practical application of transporting cosmic muons through the NOvA rock region using PROPOSAL. Additionally, an 1D analysis was conducted to determine the optimal altitude at which to hand over the simulation task from PROPOSAL to GEANT4, should we choose to use PROPOSAL for rock simulations and GEANT4 for detector simulations in the NOvA experiment. By thoroughly examining these aspects, this study aims to contribute to the advancement of muon simulation techniques and their implementation in high-energy experiments such as NOvA.
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    Muon Neutrino Contained Disappearance in NOvA
    (2015-09) Lein, Susan
    The NOvA experiment studies neutrino oscillations in the NuMI neutrino beam from Fermilab. NOvA consists of two liquid scintillator tracking calorimeters placed 14 milliradians off-axis from the beam and 810 km apart. The NOvA experiment started taking data in 2014. This thesis establishes the neutrino energy estimation procedures used to determine the oscillation parameters $\sin^2 \theta_{23}$ and $\Delta m_{32}^2$.
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    Muon Neutrino To Electron Neutrino Oscillation in NOvA
    (2015-08) Sachdev, Kanika
    NOvA is a long-baseline neutrino oscillation experiment optimized for electron neutrino appearance in the NuMI beam, a muon neutrino source at Fermilab. It consists of two functionally identical, nearly fully-active liquid-scintillator tracking calorimeters. The near detector (ND) at Fermilab is used to study the neutrino beam spectrum and composition before oscillation, and measure background rate to the electron neutrino appearance search. The far detector, 810 km away in Northern Minnesota, observes the oscillated beam and is used to extract oscillation parameters from the data. NO?A's long baseline, combined with the ability of the NuMI beam to operate in the anti-neutrino mode, makes NO?A sensitive to the last unmeasured parameters in neutrino oscillations- mass hierarchy, CP violation and the octant of mixing angle theta23. This thesis presents the search for electron neutrino appearance in the first data collected by the NO?A detectors from October 2013 till May 2015. Studies of the NuMI neutrino data collected in the NO?A near detector are also presented, which show large discrepancies between the ND simulation and data. Muon-removed electron (MRE) events, constructed by replacing the muon in muon neutrino charged current interactions by a simulated electron, are used to correct the far detector electron neutrino appearance prediction for these discrepancies. In the analysis of the first data, a total of 6 electron neutrino candidate events are observed in the far detector on a background of 1, a 3.46 sigma excess, which is interpreted as strong evidence for electron neutrino appearance. The results are consistent with our expectation, based on constraints from other neutrino oscillation experiments. The result presented here differs from the officially published electron neutrino appearance result from the NO?A experiment where the systematic error is assumed to cover the MRE correction.
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    Search for Slow Magnetic Monopoles with the NOvA Far Detector
    (2020-08) Elashri, Mohamed
    The magnetic monopole is a hypothetical particle with a magnetic charge. It is an important field configuration in many Grand Unified Theories. Dirac in 1931 derived a charge quantization condition, which suggests that the existence of one magnetic monopole in the universe will explain electric charge quantization. Since then, a search of the elusive particle has begun, and yet all came with negative results. The NOvA Far Detector is able to probe some of the parameter space for the search for monopoles. To achieve this, two dedicated triggers, one for fast monopoles and the other for slow monopoles, have been developed to record signals. Results for the first 8 months of high-gain slow monopole data were obtained, and a 90% CL limit was established with no monopole observed. The analysis in this thesis is focused on new data with a higher gain, and we have explored potential improvement in efficiency and improvements in the reconstruction algorithm. We have done this analysis on Monte-Carlo generated simulated data to establish efficiencies and examined a control set of the new data to understand its differences from the old.
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    Studies on the module structure for the NOvA liquid scintillator neutrino detector
    (2014-12) Slotman, Michael James
    This thesis contains studies pertinent to the construction of modules for the detectors of the NOvA experiment.The tensile strength of acrylic and epoxy adhesives used in construction of the detector was studied over time and with various surface preparations. Aging in oil or in air in a hot box as well as sanding and corona discharge surface treatments were examined. All materials used in the detector were studied to determine if interaction would occur with and degrade the pseudocumene liquid scintillator.Following the appearance of cracking manifold covers in installed modules, studies were done to determine the cause and mitigation of these cracks. Manifold covers that had been pressurized during a seal check during module construction were examined for the formation of cracks. Internal cracks were visible on the majority of manifold covers tested at 1.4 bar with the original containment units. It was found that reducing the pressure testing of the modules from 1.4 bar to 0.7 bar and increasing the rigidity of the pressure test containment units eliminated the formation of visible cracks in the manifold covers.
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    Using Machine Learning to Hunt for Simulated WIMPs in the NOvA Near Detector
    (2023-11) Myers, Dalton
    A neural network was trained on simulated data that included events in which electrons were scattered by hypothetical Dark Matter particles (χ) of mass mχ = 30 MeV assuming a dark vector portal mechanism of a dark photon (A') with mass mA' = 90 MeV, a gauge coupling parameter αD = 1/2, and kinetic mixing parameter e = 2 × 10 -5. The NOvA Near Detector’s response to these events was then simulated, and the pixelmaps (images) of these events occurring within the NOvA Near Detector were then used to train a machine learning algorithm designed to differentiate between the each of the ordinary observed event types that involve an electron scattered by a neutrino and hypothetical events in which an electron was scattered by a dark matter particle.