NEXT Collaboration(Ghosh, S. et al), Martin-Albo, J., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2020). Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air. J. Instrum., 15(11), P11031–16pp.
Abstract: Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. In this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm We also see that the reflectance of PIFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.
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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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Barucca, G. et al, & Diaz, J. (2021). The potential of Lambda and Xi(-) studies with PANDA at FAIR. Eur. Phys. J. A, 57(4), 154–26pp.
Abstract: The antiproton experiment PANDA at FAIR is designed to bring hadron physics to a new level in terms of scope, precision and accuracy. In this work, its unique capability for studies of hyperons is outlined. We discuss groundstate hyperons as diagnostic tools to study non-perturbative aspects of the strong interaction, and fundamental symmetries. New simulation studies have been carried out for two benchmark hyperon-antihyperon production channels: (p) over barp -> (Lambda) over bar Lambda and (p) over barp -> (Xi) over bar+Xi(-). The results, presented in detail in this paper, show that hyperon-antihyperon pairs from these reactions can be exclusively reconstructed with high efficiency and very low background contamination. In addition, the polarisation and spin correlations have been studied, exploiting the weak, self-analysing decay of hyperons and antihyperons. Two independent approaches to the finite efficiency have been applied and evaluated: one standard multidimensional efficiency correction approach, and one efficiency independent approach. The applicability of the latter was thoroughly evaluated for all channels, beam momenta and observables. The standard method yields good results in all cases, and shows that spin observables can be studied with high precision and accuracy already in the first phase of data taking with PANDA.
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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(Jones, B. J. P. et al), Carcel, S., Carrion, J. V., Diaz, J., Martin-Albo, J., Martinez, A., et al. (2022). The dynamics of ions on phased radio-frequency carpets in high pressure gases and application for barium tagging in xenon gas time projection chambers. Nucl. Instrum. Methods Phys. Res. A, 1039, 167000–19pp.
Abstract: Radio-frequency (RF) carpets with ultra-fine pitches are examined for ion transport in gases at atmospheric pressures and above. We develop new analytic and computational methods for modeling RF ion transport at densities where dynamics are strongly influenced by buffer gas collisions. An analytic description of levitating and sweeping forces from phased arrays is obtained, then thermodynamic and kinetic principles are used to calculate ion loss rates in the presence of collisions. This methodology is validated against detailed microscopic SIMION simulations. We then explore a parameter space of special interest for neutrinoless double beta decay experiments: transport of barium ions in xenon at pressures from 1 to 10 bar. Our computations account for molecular ion formation and pressure dependent mobility as well as finite temperature effects. We discuss the challenges associated with achieving suitable operating conditions, which lie beyond the capabilities of existing devices, using presently available or near-future manufacturing techniques.
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