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NEXT Collaboration(Felkai, R. et al), Sorel, M., Lopez-March, N., Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J. M., et al. (2018). Helium-Xenon mixtures to improve the topological signature in high pressure gas xenon TPCs. Nucl. Instrum. Methods Phys. Res. A, 905, 82–90.
Abstract: Within the framework of xenon-based double beta decay experiments, we propose the possibility to improve the background rejection of an electroluminescent Time Projection Chamber (EL TPC) by reducing the diffusion of the drifting electrons while keeping nearly intact the energy resolution of a pure xenon EL TPC. Based on state-of-the-art microscopic simulations, a substantial addition of helium, around 10 or 15 %, may reduce drastically the transverse diffusion down to 2.5 mm/root m from the 10.5 mm/root m of pure xenon. The longitudinal diffusion remains around 4 mm/root m. Light production studies have been performed as well. They show that the relative variation in energy resolution introduced by such a change does not exceed a few percent, which leaves the energy resolution practically unchanged. The technical caveats of using photomultipliers close to an helium atmosphere are also discussed in detail.
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XENON100 Collaboration(Aprile, E. et al), & Orrigo, S. E. A. (2014). Observation and applications of single-electron charge signals in the XENON100 experiment. J. Phys. G, 41(3), 035201–13pp.
Abstract: The XENON100 dark matter experiment uses liquid xenon in a time projection chamber (TPC) to measure xenon nuclear recoils resulting from the scattering of dark matter weakly interacting massive particles (WIMPs). In this paper, we report the observation of single-electron charge signals which are not related to WIMP interactions. These signals, which show the excellent sensitivity of the detector to small charge signals, are explained as being due to the photoionization of impurities in the liquid xenon and of the metal components inside the TPC. They are used as a unique calibration source to characterize the detector. We explain how we can infer crucial parameters for the XENON100 experiment: the secondary-scintillation gain, the extraction yield from the liquid to the gas phase and the electron drift velocity.
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Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
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