Abstract: We report the performance of a 10 atm Xenon/trimethylamine time projection chamber (TPC) for the detection of X-rays (30 keV) and gamma-rays (0.511-1.275 MeV) in conjunction with the accurate tracking of the associated electrons. When operated at such a high pressure and in similar to 1%-admixtures, trimethylamine (TMA) endows Xenon with an extremely low electron diffusion (1.3 +/- 0.13 mm-sigma (longitudinal), 0.95 +/- 0.20 mm-sigma (transverse) along 1 m drift) besides forming a convenient Penning-Fluorescent' mixture. The TPC, that houses 1.1 kg of gas in its fiducial volume, operated continuously for 100 live-days in charge amplification mode. The readout was performed through the recently introduced microbulk Micromegas technology and the AFTER chip, providing a 3D voxelization of 8 mm x 8 mm x 1.2 mm for approximately 10 cm/MeV-long electron tracks. Resolution in energy (epsilon) at full width half maximum (R) inside the fiducial volume ranged from R = 14.6% (30 keV) to R = 4.6% (1.275 MeV). This work was developed as part of the R&D program of the NEXT collaboration for future detector upgrades in the search of the neutrino-less double beta decay (beta beta 0 nu) in Xe-136, specifically those based on novel gas mixtures. Therefore we ultimately focus on the calorimetric and topological properties of the reconstructed MeV-electron tracks. In particular, the obtained energy resolution has been decomposed in its various contributions and improvements towards achieving the R =1.4%root MeV/epsilon levels obtained in small sensors are discussed.

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.

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.