T2K Collaboration(Abe, K. et al), Antonova, M., Cervera-Villanueva, A., Fernandez, P., Izmaylov, A., & Novella, P. (2020). First measurement of the charged current (nu)over-bar(mu) double differential cross section on a water target without( )pions in the final state. Phys. Rev. D, 102(1), 012007–16pp.
Abstract: This paper reports the first differential measurement of the charged-current (nu) over bar (mu) interaction cross section on water with no pions in the final state. The unfolded flux-averaged measurement using the T2K off-axis near detector is given in double-differential bins of mu(+) momentum and angle. The integrated cross section in a restricted phase space is sigma = (1.11 +/- 0.18) x 10(-38) cm(2) per water molecule. Comparisons with several nuclear models are also presented.
|
Double Chooz collaboration(Abrahao, T. et al), & Novella, P. (2021). Reactor rate modulation oscillation analysis with two detectors in Double Chooz. J. High Energy Phys., 01(1), 190–18pp.
Abstract: A theta (13) oscillation analysis based on the observed antineutrino rates at the Double Chooz far and near detectors for different reactor power conditions is presented. This approach provides a so far unique simultaneous determination of theta (13) and the total background rates without relying on any assumptions on the specific background contributions. The analysis comprises 865 days of data collected in both detectors with at least one reactor in operation. The oscillation results are enhanced by the use of 24.06 days (12.74 days) of reactor-off data in the far (near) detector. The analysis considers the nu <mml:mo stretchy=“true”><overbar></mml:mover>e interactions up to a visible energy of 8.5 MeV, using the events at higher energies to build a cosmogenic background model considering fast-neutrons interactions and Li-9 decays. The background-model-independent determination of the mixing angle yields sin(2)(2 theta (13)) = 0.094 0.017, being the best-fit total background rates fully consistent with the cosmogenic background model. A second oscillation analysis is also performed constraining the total background rates to the cosmogenic background estimates. While the central value is not significantly modified due to the consistency between the reactor-off data and the background estimates, the addition of the background model reduces the uncertainty on theta (13) to 0.015. Along with the oscillation results, the normalization of the anti-neutrino rate is measured with a precision of 0.86%, reducing the 1.43% uncertainty associated to the expectation.
|
NEXT Collaboration(Kekic, M. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys., 01(1), 189–22pp.
Abstract: Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.
|
DUNE Collaboration(Abud, A. A. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2022). Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC. J. Instrum., 17(1), P01005–111pp.
Abstract: The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 x 6 x 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.
Keywords: Noble liquid detectors (scintillation, ionization, double-phase); Photon detectors for UV; visible and IR photons (solid-state) (PIN diodes, APDs, Si-PMTs, G-APDs, CCDs, EBCCDs, EMCCDs, CMOS imagers, etc); Scintillators; scintillation and light emission processes (solid, gas and liquid scintillators); Time projection Chambers (TPC)
|
Novella, P. (2015). The antineutrino energy structure in reactor experiments. Adv. High. Energy Phys., 2015, 364392–12pp.
Abstract: The recent observation of an energy structure in the reactor antineutrino spectrum is reviewed. The reactor experiments Daya Bay, Double Chooz, and RENO have reported a consistent excess of antineutrinos deviating from the flux predictions, with a local significance of about 4 sigma between 4 and 6 MeV of the positron energy spectrum. The possible causes of the structure are analyzed in this work, along with the different experimental approaches developed to identify its origin. Considering the available data and results from the three experiments, the most likely explanation concerns the reactor flux predictions and the associated uncertainties. Therefore, the different current models are described and compared. The possible sources of incompleteness or inaccuracy of such models are discussed, as well as the experimental data required to improve their precision.
|
Double Chooz collaboration(Abe, Y. et al), & Novella, P. (2016). Characterization of the spontaneous light emission of the PMTs used in the Double Chooz experiment. J. Instrum., 11, P08001–25pp.
Abstract: During the commissioning of the first of the two detectors of the Double Chooz experiment, an unexpected and dominant background caused by the emission of light inside the optical volume has been observed. A specific study of the ensemble of phenomena called Light Noise has been carried out in-situ, and in an external laboratory, in order to characterize the signals and to identify the possible processes underlying the effect. Some mechanisms of instrumental noise originating from the PMTs were identified and it has been found that the leading one arises from the light emission localized on the photomultiplier base and produced by the combined effect of heat and high voltage across the transparent epoxy resin covering the electric components. The correlation of the rate and the amplitude of the signal with the temperature has been observed. For the first detector in operation the induced background has been mitigated using online and offline analysis selections based on timing and light pattern of the signals, while a modification of the photomultiplier assembly has been implemented for the second detector in order to blacken the PMT bases.
|
NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
|
NEXT Collaboration(Henriques, C. A. O. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J., Botas, A., Carcel, S., et al. (2017). Secondary scintillation yield of xenon with sub-percent levels of CO2 additive for rare-event detection. Phys. Lett. B, 773, 663–671.
Abstract: Xe-CO2 mixtures are important alternatives to pure xenon in Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification with applications in the important field of rare event detection such as directional dark matter, double electron capture and double beta decay detection. The addition of CO2 to pure xenon at the level of 0.05-0.1% can reduce significantly the scale of electron diffusion from 10 mm/root m to 2.5 mm/root m, with high impact on the discrimination of the events through pattern recognition of the topology of primary ionization trails. We have measured the electroluminescence (EL) yield of Xe-CO2 mixtures, with sub-percent CO2 concentrations. We demonstrate that the EL production is still high in these mixtures, 70% and 35% relative to that produced in pure xenon, for CO2 concentrations around 0.05% and 0.1%, respectively. The contribution of the statistical fluctuations in EL production to the energy resolution increases with increasing CO2 concentration, being smaller than the contribution of the Fano factor for concentrations below 0.1% CO2.
|
NEXT Collaboration(Cebrian, S. et al), Perez, J., Alvarez, V., Benlloch-Rodriguez, J., Botas, A., Carcel, S., et al. (2017). Radiopurity assessment of the energy readout for the NEXT double beta decay experiment. J. Instrum., 12, T08003–20pp.
Abstract: The “Neutrino Experiment with a Xenon Time-Projection Chamber” (NEXT) experiment intends to investigate the neutrinoless double beta decay of Xe-136, and therefore requires a severe suppression of potential backgrounds. An extensive material screening and selection process was undertaken to quantify the radioactivity of the materials used in the experiment. Separate energy and tracking readout planes using different sensors allow us to combine the measurement of the topological signature of the event for background discrimination with the energy resolution optimization. The design of radiopure readout planes, in direct contact with the gas detector medium, was especially challenging since the required components typically have activities too large for experiments demanding ultra-low background conditions. After studying the tracking plane, here the radiopurity control of the energy plane is presented, mainly based on gamma-ray spectroscopy using ultra-low background germanium detectors at the Laboratorio Subterraneo de Canfranc (Spain). All the available units of the selected model of photomultiplier have been screened together with most of the components for the bases, enclosures and windows. According to these results for the activity of the relevant radioisotopes, the selected components of the energy plane would give a contribution to the overall background level in the region of interest of at most 2.4 x 10(-4) counts keV(-1) kg(-1) y(-1), satisfying the sensitivity requirements of the NEXT experiment.
|
NEXT Collaboration(Simon, A. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2017). Application and performance of an ML-EM algorithm in NEXT. J. Instrum., 12, P08009–22pp.
Abstract: The goal of the NEXT experiment is the observation of neutrinoless double beta decay in Xe-136 using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.
|
