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Herrero, V., Toledo, J., Catala, J. M., Esteve, R., Gil, A., Lorca, D., et al. (2012). Readout electronics for the SiPM tracking plane in the NEXT-1 prototype. Nucl. Instrum. Methods Phys. Res. A, 695, 229–232.
Abstract: NEXT is a new experiment to search for neutrinoless double beta decay using a 100 kg radio-pure high-pressure gaseous xenon TPC with electroluminescence readout. A large-scale prototype with a SiPM tracking plane has been built. The primary electron paths can be reconstructed from time-resolved measurements of the light that arrives to the SiPM plane. Our approach is to measure how many photons have reached each SiPM sensor each microsecond with a gated integrator. We have designed and tested a 16-channel front-end board that includes the analog paths and a digital section. Each analog path consists of three different stages: a transimpedance amplifier, a gated integrator and an offset and gain control stage. Measurements show good linearity and the ability to detect single photoelectrons.
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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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NEXT Collaboration(Renner, J. et al), Alvarez, V., Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., et al. (2015). Ionization and scintillation of nuclear recoils in gaseous xenon. Nucl. Instrum. Methods Phys. Res. A, 793, 62–74.
Abstract: Ionization and scintillation produced by nuclear recoils in gaseous xenon at approximately 14 bar have been simultaneously observed in an electroluminescent time projection chamber. Neutrons from radioisotope a-Be neutron sources were used to induce xenon nuclear recoils, and the observed recoil spectra were compared to a detailed Monte Carlo employing estimated ionization and scintillation yields for nuclear recoils. The ability to discriminate between electronic and nuclear recoils using the ratio of ionization to primary scintillation is demonstrated. These results encourage further investigation on the use of xenon in the gas phase as a detector medium in dark matter direct detection experiments.
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