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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.
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NEXT Collaboration(Simon, A. et al), Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., Lopez-March, N., et al. (2021). Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution. J. High Energy Phys., 07(7), 146–38pp.
Abstract: Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of similar to 10(27) yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of <similar to> 5 when reconstructing electron-positron pairs in the Tl-208 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterraneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of similar to 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e(-)e(+) pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.
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NEXT Collaboration(Lorca, D. et al), Martin-Albo, J., Laing, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2014). Characterisation of NEXT-DEMO using xenon K-alpha X-rays. J. Instrum., 9, P10007–20pp.
Abstract: The NEXT experiment aims to observe the neutrinoless double beta decay of Xe-136 in a high-pressure xenon gas TPC using electroluminescence (EL) to amplify the signal from ionization. Understanding the response of the detector is imperative in achieving a consistent and well understood energy measurement. The abundance of xenon K-shell X-ray emission during data taking has been identified as a multitool for the characterisation of the fundamental parameters of the gas as well as the equalisation of the response of the detector. The NEXT-DEMO prototype is a similar to 1.5 kg volume TPC filled with natural xenon. It employs an array of 19 PMTs as an energy plane and of 256 SiPMs as a tracking plane with the TPC light tube and SiPM surfaces being coated with tetraphenyl butadiene (TPB) which acts as a wavelength shifter for the VUV scintillation light produced by xenon. This paper presents the measurement of the properties of the drift of electrons in the TPC, the effects of the EL production region, and the extraction of position dependent correction constants using K-alpha X-ray deposits. These constants were used to equalise the response of the detector to deposits left by gammas from Na-22.
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Andreotti, M. et al, Cervera-Villanueva, A., Garcia-Peris, M. a., Martin-Albo, J., Querol, M., Rocabado, J., et al. (2024). Cryogenic characterization of Hamamatsu HWB MPPCs for the DUNE photon detection system. J. Instrum., 19(1), T01007–27pp.
Abstract: The Deep Underground Neutrino Experiment (DUNE) is a next generation experiment aimed to study neutrino oscillation. Its long-baseline configuration will exploit a Near Detector (ND) and a Far Detector (FD) located at a distance of similar to 1300 km. The FD will consist of four Liquid Argon Time Projection Chamber (LAr TPC) modules. A Photon Detection System (PDS) will be used to detect the scintillation light produced inside the detector after neutrino interactions. The PDS will be based on light collectors coupled to Silicon Photomultipliers (SiPMs). Different photosensor technologies have been proposed and produced in order to identify the best samples to fullfill the experiment requirements. In this paper, we present the procedure and results of a validation campaign for the Hole Wire Bonding (HWB) MPPCs samples produced by Hamamatsu Photonics K.K. (HPK) for the DUNE experiment, referring to them as 'SiPMs'. The protocol for a characterization at cryogenic temperature (77 K) is reported. We present the down-selection criteria and the results obtained during the selection campaign undertaken, along with a study of the main sources of noise of the SiPMs including the investigation of a newly observed phenomenon in this field.
Keywords: Cryogenic detectors; Photon detectors for UV, visible and IR photons (solid-state); Photon detectors for UV, visible and IR photons (solid-state) (PIN diodes, APDs, Si-PMTs, G-APDs, CCDs, EBCCDs, EMCCDs, CMOS imagers, etc)
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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.
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