45 research outputs found

    Long-term and seasonal variations in CO2: linkages to catchment alkalinity generation

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    International audienceAs atmospheric emissions of S have declined in the Northern Hemisphere, there has been an expectation of increased pH and alkalinity in streams believed to have been acidified by excess S and N. Many streams and lakes have not recovered. Evidence from East Bear Brook in Maine, USA and modelling with the groundwater acid-base model MAGIC (Cosby et al. 1985a,b) indicate that seasonal and yearly variations in soil PCO2 are adequate to enhance or even reverse acid-base (alkalinity) changes anticipated from modest decreases of SO4 in surface waters. Alkalinity is generated in the soil by exchange of H+ from dissociation of H2CO3, which in turn is derived from the dissolving of soil CO2. The variation in soil PCO2 produces an alkalinity variation of up to 15 meq L-1 in stream water. Detecting and relating increases in alkalinity to decreases in stream SO4 are significantly more difficult in the short term because of this effect. For example, modelled alkalinity recovery at Bear Brook due to a decline of 20 meq SO4 L-1 in soil solution is compensated by a decline from 0.4 to 0.2% for soil air PCO2. This compensation ability decays over time as base saturation declines. Variable PCO2 has less effect in more acidic soils. Short-term decreases of PCO2 below the long-term average value produce short-term decreases in alkalinity, whereas short-term increases in PCO2 produce short-term alkalization. Trend analysis for detecting recovery of streams and lakes from acidification after reduced atmospheric emissions will require a longer monitoring period for statistical significance than previously appreciated. Keywords: CO2 , alkalinity, acidification, recovery, soils, climate chang

    Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

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    [EN] 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 analysesThis study used computing resources from Artemisa, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following agencies and institutions: Xunta de Galicia (Centro singularde investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the "Maria de Maeztu" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency"; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-20140398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Kekic, M.; Adams, C.; Woodruff, K.; Renner, J.; Church, E.; Del Tutto, M.; Hernando Morata, JA.... (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. Journal of High Energy Physics (Online). (1):1-22. https://doi.org/10.1007/JHEP01(2021)189S1221NEXT collaboration, The Next White (NEW) Detector, 2018 JINST 13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of 136Xe, JHEP 10 (2019) 230 [arXiv:1905.13110] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, JHEP 10 (2019) 052 [arXiv:1905.13141] [INSPIRE].NEXT collaboration, Radiogenic Backgrounds in the NEXT Double Beta Decay Experiment, JHEP 10 (2019) 051 [arXiv:1905.13625] [INSPIRE].G. Carleo et al., Machine learning and the physical sciences, Rev. Mod. Phys. 91 (2019) 045002 [arXiv:1903.10563] [INSPIRE].A. Aurisano et al., A Convolutional Neural Network Neutrino Event Classifier, 2016 JINST 11 P09001 [arXiv:1604.01444] [INSPIRE].MicroBooNE collaboration, Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber, 2017 JINST 12 P03011 [arXiv:1611.05531] [INSPIRE].MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber, Phys. Rev. D 99 (2019) 092001 [arXiv:1808.07269] [INSPIRE].N. Choma et al., Graph Neural Networks for IceCube Signal Classification, in proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, U.S.A., 17–20 December 2018, pp. 386–391 [arXiv:1809.06166] [INSPIRE].E. Racah et al., Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, in proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, U.S.A., 18–20 December 2016, pp. 892–897 [arXiv:1601.07621] [INSPIRE].EXO collaboration, Deep Neural Networks for Energy and Position Reconstruction in EXO-200, 2018 JINST 13 P08023 [arXiv:1804.09641] [INSPIRE].H. Qiao, C. Lu, X. Chen, K. Han, X. Ji and S. Wang, Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron. 61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].P. Ai, D. Wang, G. Huang and X. Sun, Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber, 2018 JINST 13 P08015 [arXiv:1803.01482] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST 12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP 05 (2016) 159 [arXiv:1511.09246] [INSPIRE].D. Nygren, High-pressure xenon gas electroluminescent TPC for 0-ν ββ-decay search, Nucl. Instrum. Meth. A 603 (2009) 337 [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using 83mKr decays, 2018 JINST 13 P10014 [arXiv:1804.01780] [INSPIRE].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. Thesis, University of Valencia, Valencia Spain (2015) [INSPIRE].GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [INSPIRE].C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On calibration of modern neural networks, arXiv:1706.04599.K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].K. He, X. Zhang, S. Ren and J. Sun, Identity mappings in deep residual networks, arXiv:1603.05027.X. Li, S. Chen, X. Hu and J. Yang, Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift, in proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, U.S.A., 15–20 June 2019, pp. 2677–2685.J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, U.S.A., 20–25 June 2009, pp. 248–255.B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102 (2020) 012005 [arXiv:1903.05663] [INSPIRE].C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019) 60.G.J. Székely and M.L. Rizzo, Testing for equal distributions in high dimension, InterStat 5 (2004) 1.G. Székely and M.L. Rizzo, Energy statistics: A class of statistics based on distances, J. Stat. Plann. Infer. 8 (2013) 1249.R.A. Fisher, The Design of Experiments, Oliver and Boyd (1935).NEXT collaboration, Sensitivity of a tonne-scale NEXT detector for neutrinoless double beta decay searches, arXiv:2005.06467 [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST 8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST 8 P09011 [arXiv:1306.0471] [INSPIRE]

    Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield

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    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 ∼ 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. [Figure not available: see fulltext.]

    Energy calibration of the NEXT-White detector with 1% resolution near Q ββ of 136Xe

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    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 (ββ0ν), 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 ββ0ν searches. [Figure not available: see fulltext.

    Demonstration of the event identification capabilities of the NEXT-White detector

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    [EN] 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 228Th 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.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program SEV-2014-0398 and the Maria de Maetzu Program MDM-2016-0692; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014, under project UID/FIS/04559/2013 to fund the activities of LIBPhys, and under grants PD/BD/FBD/105921/2014, SFRH/BPD/109180/2015 and SFRH/BPD/76842/2011; the U.S. Department of Energy under contracts number DE-AC02-06CH11357 (Argonne National Laboratory), DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015-18820. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Ferrario, P.; Benlloch-Rodríguez, J.; Díaz López, G.; Hernando Morata, J.; Kekic, M.; Renner, J.; Usón, A.... (2019). Demonstration of the event identification capabilities of the NEXT-White detector. Journal of High Energy Physics (Online). (10):1-17. https://doi.org/10.1007/JHEP10(2019)052S11710M. Fukugita and T. Yanagida, Baryogenesis without grand unification, Phys. Lett.B 174 (1986) 45 [ INSPIRE ].EXO-200 collaboration, Improved measurement of the 2νββ half-life of136Xe with the EXO-200 detector, Phys. Rev.C 89 (2014) 015502 [ arXiv:1306.6106 ] [ INSPIRE ].XENON collaboration, Dark matter search results from a one ton-year exposure of XENON1T, Phys. Rev. Lett.121 (2018) 111302 [ arXiv:1805.12562 ] [ INSPIRE ].Caltech-Neuchâtel-PSI collaboration, Search for ββ decay in136Xe: new results from the Gotthard experiment, Phys. Lett.B 434 (1998) 407 [ INSPIRE ].NEXT collaboration, First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment, JHEP01 (2016) 104 [ arXiv:1507.05902 ] [ INSPIRE ].NEXT collaboration, The Next White (NEW) detector, 2018 JINST13 P12010 [ arXiv:1804.02409 ] [ INSPIRE ].M. Redshaw, E. Wingfield, J. McDaniel and E.G. Myers, Mass and double-beta-decay Q value of136Xe, Phys. Rev. Lett.98 (2007) 053003 [ INSPIRE ].NEXT collaboration, Initial results on energy resolution of the NEXT-White detector, 2018 JINST13 P10020 [ arXiv:1808.01804 ] [ INSPIRE ].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of136Xe, arXiv:1905.13110 [ INSPIRE ].NEXT collaboration, Electron drift properties in high pressure gaseous xenon, 2018 JINST13 P07013 [ arXiv:1804.01680 ] [ INSPIRE ].T.H. Cormen, C. Stein, R.L. Rivest and C.E. Leiserson, Introduction to algorithms, 2nd ed., McGraw-Hill Higher Education, U.S.A. (2001).NEXT collaboration, Calibration of the NEXT-White detector using83mKr decays, 2018 JINST13 P10014 [ arXiv:1804.01780 ] [ INSPIRE ].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. thesis, Valencia U., IFIC, Valencia, Spain (2015).GEANT4 collaboration, GEANT4: a simulation toolkit, Nucl. Instrum. Meth.A 506 (2003) 250 [ INSPIRE ].J.J. Gomez-Cadenas et al., Sense and sensitivity of double beta decay experiments, JCAP06 (2011) 007 [ arXiv:1010.5112 ] [ INSPIRE ].NEXT collaboration, Radiogenic backgrounds in the NEXT double beta decay experiment, arXiv:1905.13625 [ INSPIRE ].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST12 T01004 [ arXiv:1609.06202 ] [ INSPIRE ].NEXT collaboration, Application and performance of an ML-EM algorithm in NEXT, 2017 JINST12 P08009 [ arXiv:1705.10270 ] [ INSPIRE ].NEXT collaboration, Secondary scintillation yield of xenon with sub-percent levels of CO2 additive for rare-event detection, Phys. Lett.B 773 (2017) 663 [ arXiv:1704.01623 ] [ INSPIRE ].NEXT collaboration, Electroluminescence TPCs at the thermal diffusion limit, JHEP01 (2019) 027 [ arXiv:1806.05891 ] [ INSPIRE ].R. Felkai et al., Helium-xenon mixtures to improve the topological signature in high pressure gas xenon TPCs, Nucl. Instrum. Meth.A 905 (2018) 82 [ arXiv:1710.05600 ] [ INSPIRE ].NEXT collaboration, Electron drift and longitudinal diffusion in high pressure xenon-helium gas mixtures, 2019 JINST14 P08009 [ arXiv:1902.05544 ] [ INSPIRE ].NEXT collaboration, Sensitivity of NEXT-100 to neutrinoless double beta decay, JHEP05 (2016) 159 [ arXiv:1511.09246 ] [ INSPIRE ].J. Muñoz Vidal, The NEXT path to neutrino inverse hierarchy, Ph.D. thesis, Valencia U., IFIC, Valencia, Spain (2018)

    Sensitivity of the NEXT experiment to Xe-124 double electron capture

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    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¿EC EC) has been predicted for a number of isotopes, but only observed in 78Kr, 130Ba and, recently, 124Xe. 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¿EC EC. Here we report on the current sensitivity of the NEXT-White detector to 124Xe 2¿EC EC and on the extrapolation to NEXT-100. Using simulated data for the 2¿EC EC signal and real data from NEXT-White operated with 124Xe-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 124Xe and for a 5-year run, a sensitivity to the 2¿EC EC half-life of 6 × 1022 y (at 90% confidence level) or better can be reached. [Figure not available: see fulltext.

    Energy calibration of the NEXT-White detector with 1% resolution near Qßß of 136Xe

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    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 (ßß0¿), 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 ßß0¿ searches. [Figure not available: see fulltext

    Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield

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    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 ~ 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

    Radiogenic backgrounds in the NEXT double beta decay experiment

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    Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterráneo de Canfranc with xenon depleted in 136Xe are analyzed to derive a total background rate of (0.84±0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of 60Co, 40K, 214Bi and 208Tl to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25±0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5s after 1 year of data taking. The background measurement in a Qßß±100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75±0.12) events

    Mitigation of backgrounds from cosmogenic 137Xe in xenon gas experiments using 3He neutron capture

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    136Xe is used as the target medium for many experiments searching for 0¿ßß. Despite underground operation, cosmic muons that reach the laboratory can produce spallation neutrons causing activation of detector materials. A potential background that is difficult to veto using muon tagging comes in the form of 137Xe created by the capture of neutrons on 136Xe. This isotope decays via beta decay with a half-life of 3.8 min and a Q ß of ~4.16 MeV. This work proposes and explores the concept of adding a small percentage of 3He to xenon as a means to capture thermal neutrons and reduce the number of activations in the detector volume. When using this technique we find the contamination from 137Xe activation can be reduced to negligible levels in tonne and multi-tonne scale high pressure gas xenon neutrinoless double beta decay experiments running at any depth in an underground laboratory
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