NEXT Collaboration(Renner, J. et al), Kekic, M., Martinez-Lema, G., Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., et al. (2019). Energy calibration of the NEXT-White detector with 1% resolution near Q(beta beta) of Xe-136. J. High Energy Phys., 10(10), 230–13pp.
Abstract: Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (beta beta 0 nu), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for beta beta 0 nu searches.
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NEXT Collaboration(Ferrario, P. et al), Benlloch-Rodriguez, J. M., Kekic, M., Renner, J., Uson, A., Alvarez, V., et al. (2019). Demonstration of the event identification capabilities of the NEXT-White detector. J. High Energy Phys., 10(10), 052–20pp.
Abstract: In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a Th-228 calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of 71.6 +/- 1.5(stat) +/- 0.3(sys) % for a background acceptance of 20.6 +/- 0.4(stat) +/- 0.3(sys)% is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies.
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NEXT Collaboration(Fernandes, A. F. M. et al), Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., et al. (2020). Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield. J. High Energy Phys., 04(4), 034–18pp.
Abstract: High pressure xenon Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification are being proposed for rare event detection such as directional dark matter, double electron capture and double beta decay detection. The discrimination of the rare event through the topological signature of primary ionisation trails is a major asset for this type of TPC when compared to single liquid or double-phase TPCs, limited mainly by the high electron diffusion in pure xenon. Helium admixtures with xenon can be an attractive solution to reduce the electron diffu- sion significantly, improving the discrimination efficiency of these optical TPCs. We have measured the electroluminescence (EL) yield of Xe-He mixtures, in the range of 0 to 30% He and demonstrated the small impact on the EL yield of the addition of helium to pure xenon. For a typical reduced electric field of 2.5 kV/cm/bar in the EL region, the EL yield is lowered by similar to 2%, 3%, 6% and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This decrease is less than what has been obtained from the most recent simulation framework in the literature. The impact of the addition of helium on EL statistical fluctuations is negligible, within the experimental uncertainties. The present results are an important benchmark for the simulation tools to be applied to future optical TPCs based on Xe-He mixtures.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Search for t(t)over-bar resonances in fully hadronic final states in pp collisions at root s=13 TeV with the ATLAS detector. J. High Energy Phys., 10(10), 061–43pp.
Abstract: This paper presents a search for new heavy particles decaying into a pair of top quarks using 139 fb(-1) of proton-proton collision data recorded at a centre-of-mass energy of root s = 13TeV with the ATLAS detector at the Large Hadron Collider. The search is performed using events consistent with pair production of high-transverse-momentum top quarks and their subsequent decays into the fully hadronic final states. The analysis is optimized for resonances decaying into a t (t) over bar pair with mass above 1.4TeV, exploiting a dedicated multivariate technique with jet substructure to identify hadronically decaying top quarks using large-radius jets and evaluating the background expectation from data. No significant deviation from the background prediction is observed. Limits are set on the production cross-section times branching fraction for the new Z' boson in a topcolor-assisted-technicolor model. The Z0 boson masses below 3.9 and 4.7TeV are excluded at 95% confidence level for the decay widths of 1% and 3%, respectively.
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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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