|
|
DUNE Collaboration(Abud, A. A. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2022). Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC. Eur. Phys. J. C, 82(7), 618–29pp.
Abstract: DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 x 6 x 6 m(3) liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.
|
|
|
|
DUNE Collaboration(Abud, A. A. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2022). Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. Eur. Phys. J. C, 82(10), 903–19pp.
Abstract: Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
|
|
|
|
DUNE Collaboration(Abud, A. A. et al), Amedo, P., Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., et al. (2023). Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora. Eur. Phys. J. C, 83(7), 618–25pp.
Abstract: The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 +/- 0.6% and 84.1 +/- 0.6%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.
|
|
|
|
Schwetz, T., Tortola, M., & Valle, J. W. F. (2011). Global neutrino data and recent reactor fluxes: the status of three-flavour oscillation parameters. New J. Phys., 13, 063004–15pp.
Abstract: We present the results of a global neutrino oscillation data analysis within the three-flavour framework. We include the latest results from the MINOS long-baseline experiment (including electron neutrino appearance and anti-neutrino data), updating all relevant solar (Super-Kamiokande (SK) II + III), atmospheric (SK I + II + III) and reactor (KamLAND) data. Furthermore, we include a recent re-calculation of the anti-neutrino fluxes emitted from nuclear reactors. These results have important consequences for the analysis of reactor experiments and in particular for the status of the mixing angle theta(13). In our recommended default analysis, we find from the global fit that the hint for nonzero theta(13) remains weak, at 1.8 sigma for both neutrino mass hierarchy schemes. However, we discuss in detail the dependence of these results on assumptions regarding the reactor neutrino analysis.
|
|
|
|
Schwetz, T., Tortola, M., & Valle, J. W. F. (2011). Where we are on theta(13): addendum to 'Global neutrino data and recent reactor fluxes: status of three-flavor oscillation parameters'. New J. Phys., 13, 109401–5pp.
Abstract: In this addendum to Schwetz et al (2011 New J. Phys. 13 063004), we consider the recent results from long-baseline nu(mu) -> nu(e) searches at the Tokai to Kamioka (T2K) and Main Injector Neutrino Oscillation Search (MINOS) experiments and investigate their implications for the mixing angle theta(13) and the leptonic Dirac CP phase delta. By combining the 2.5 sigma indication for a nonzero value of theta(13) coming from the T2K data with global neutrino oscillation data, we obtain a significance for theta(13) > 0 of about 3 sigma with best fit points sin(2) theta(13) = 0.013 (0.016) for normal (inverted) neutrino mass ordering. These results depend somewhat on assumptions concerning the analysis of reactor neutrino data.
|
|
