NEXT Collaboration(Martinez-Lema, G. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Sensitivity of the NEXT experiment to Xe-124 double electron capture. J. High Energy Phys., 02(2), 203–25pp.
Abstract: Double electron capture by proton-rich nuclei is a second-order nuclear process analogous to double beta decay. Despite their similarities, the decay signature is quite different, potentially providing a new channel to measure the hypothesized neutrinoless mode of these decays. The Standard-Model-allowed two-neutrino double electron capture (2 nu EC EC) has been predicted for a number of isotopes, but only observed in Kr-78, Ba-130 and, recently, Xe-124. The sensitivity to this decay establishes a benchmark for the ultimate experimental goal, namely the potential to discover also the lepton-number-violating neutrinoless version of this process, 0 nu EC EC. Here we report on the current sensitivity of the NEXT-White detector to Xe-124 2 nu EC EC and on the extrapolation to NEXT-100. Using simulated data for the 2 nu EC EC signal and real data from NEXT-White operated with Xe-124-depleted gas as background, we define an optimal event selection that maximizes the NEXT-White sensitivity. We estimate that, for NEXT-100 operated with xenon gas isotopically enriched with 1 kg of Xe-124 and for a 5-year run, a sensitivity to the 2 nu EC EC half-life of 6 x 10(22) y (at 90% confidence level) or better can be reached.
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NEXT Collaboration(Adams, C. et al), Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., Lopez-March, N., et al. (2021). Sensitivity of a tonne-scale NEXT detector for neutrinoless double-beta decay searches. J. High Energy Phys., 08(8), 164–24pp.
Abstract: The Neutrino Experiment with a Xenon TPC (NEXT) searches for the neutrinoless double-beta (0 nu beta beta) decay of Xe-136 using high-pressure xenon gas TPCs with electroluminescent amplification. A scaled-up version of this technology with about 1 tonne of enriched xenon could reach in less than 5 years of operation a sensitivity to the half-life of 0 nu beta beta decay better than 10(27) years, improving the current limits by at least one order of magnitude. This prediction is based on a well-understood background model dominated by radiogenic sources. The detector concept presented here represents a first step on a compelling path towards sensitivity to the parameter space defined by the inverted ordering of neutrino masses, and beyond.
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Melcon, A. A., Cuendis, S. A., Cogollos, C., Diaz-Morcillo, A., Dobrich, B., Gallego, J. D., et al. (2020). Scalable haloscopes for axion dark matter detection in the 30 μeV range with RADES. J. High Energy Phys., 07(7), 084–28pp.
Abstract: RADES (Relic Axion Detector Exploratory Setup) is a project with the goal of directly searching for axion dark matter above the 30 μeV scale employing custom-made microwave filters in magnetic dipole fields. Currently RADES is taking data at the LHC dipole of the CAST experiment. In the long term, the RADES cavities are envisioned to take data in the BabyIAXO magnet. In this article we report on the modelling, building and characterisation of an optimised microwave-filter design with alternating irises that exploits maximal coupling to axions while being scalable in length without suffering from mode-mixing. We develop the mathematical formalism and theoretical study which justifies the performance of the chosen design. We also point towards the applicability of this formalism to optimise the MADMAX dielectric haloscopes.
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ANTARES Collaboration(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2020). Search for dark matter towards the Galactic Centre with 11 years of ANTARES data. Phys. Lett. B, 805, 135439–6pp.
Abstract: Neutrino detectors participate in the indirect search for the fundamental constituents of dark matter (DM) in form of weakly interacting massive particles (WIMPs). In WIMP scenarios, candidate DM particles can pair-annihilate into Standard Model products, yielding considerable fluxes of high-energy neutrinos. A detector like ANTARES, located in the Northern Hemisphere, is able to perform a complementary search looking towards the Galactic Centre, where a high density of dark matter is thought to accumulate. Both this directional information and the spectral features of annihilating DM pairs are entered into an unbinned likelihood method to scan the data set in search for DM-like signals in ANTARES data. Results obtained upon unblinding 3170 days of data reconstructed with updated methods are presented, which provides a larger, and more accurate, data set than a previously published result using 2101 days. A non-observation of dark matter is converted into limits on the velocity-averaged cross section for WIMP pair annihilation.
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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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