Performance of the Muon Identification at LHCb

The performance of the muon identification in LHCb is extracted from data using muons and hadrons produced in J/\psi->\mu\mu, \Lambda->p\pi and D^{\star}->\pi D0(K\pi) decays. The muon identification procedure is based on the pattern of hits in the muon chambers. A momentum dependent binary requirement is used to reduce the probability of hadrons to be misidentified as muons to the level of 1%, keeping the muon efficiency in the range of 95-98%. As further refinement, a likelihood is built for the muon and non-muon hypotheses. Adding a requirement on this likelihood that provides a total muon efficiency at the level of 93%, the hadron misidentification rates are below 0.6%.Comment: 17 pages, 10 figure

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

[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). 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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. 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Evaluation of turbulent dissipation rate retrievals from Doppler Cloud Radar

Turbulent dissipation rate retrievals from cloud radar Doppler velocity measurements are evaluated using independent, in situ observations in Arctic stratocumulus clouds. In situ validation data sets of dissipation rate are derived using sonic anemometer measurements from a tethered balloon and high frequency pressure variation observations from a research aircraft, both flown in proximity to stationary, ground-based radars. Modest biases are found among the data sets in particularly low- or high-turbulence regimes, but in general the radar-retrieved values correspond well with the in situ measurements. Root mean square differences are typically a factor of 4-6 relative to any given magnitude of dissipation rate. These differences are no larger than those found when comparing dissipation rates computed from tetheredballoon and meteorological tower-mounted sonic anemometer measurements made at spatial distances of a few hundred meters. Temporal lag analyses suggest that approximately half of the observed differences are due to spatial sampling considerations, such that the anticipated radar-based retrieval uncertainty is on the order of a factor of 2-3. Moreover, radar retrievals are clearly able to capture the vertical dissipation rate structure observed by the in situ sensors, while offering substantially more information on the time variability of turbulence profiles. Together these evaluations indicate that radar-based retrievals can, at a minimum, be used to determine the vertical structure of turbulence in Arctic stratocumulus clouds

Radon and material radiopurity assessment for the NEXT double beta decay experiment

The Neutrino Experiment with a Xenon TPC (NEXT), intended to investigate the neutrinoless double beta decay using a high-pressure xenon gas TPC filled with Xe enriched in 136Xe at the Canfranc Underground Laboratory in Spain, requires ultra-low background conditions demanding an exhaustive control of material radiopurity and environmental radon levels. An extensive material screening process is underway for several years based mainly on gamma-ray spectroscopy using ultra-low background germanium detectors in Canfranc but also on mass spectrometry techniques like GDMS and ICPMS. Components from shielding, pressure vessel, electroluminescence and high voltage elements and energy and tracking readout planes have been analyzed, helping in the final design of the experiment and in the construction of the background model. The latest measurements carried out will be presented and the implication on NEXT of their results will be discussed. The commissioning of the NEW detector, as a first step towards NEXT, has started in Canfranc; in-situ measurements of airborne radon levels were taken there to optimize the system for radon mitigation and will be shown too.Comment: Proceedings of the Low Radioactivity Techniques 2015 workshop (LRT2015), Seattle, March 201

NEXT-100 Technical Design Report (TDR). Executive Summary

In this Technical Design Report (TDR) we describe the NEXT-100 detector that will search for neutrinoless double beta decay (bbonu) in Xe-136 at the Laboratorio Subterraneo de Canfranc (LSC), in Spain. The document formalizes the design presented in our Conceptual Design Report (CDR): an electroluminescence time projection chamber, with separate readout planes for calorimetry and tracking, located, respectively, behind cathode and anode. The detector is designed to hold a maximum of about 150 kg of xenon at 15 bar, or 100 kg at 10 bar. This option builds in the capability to increase the total isotope mass by 50% while keeping the operating pressure at a manageable level. The readout plane performing the energy measurement is composed of Hamamatsu R11410-10 photomultipliers, specially designed for operation in low-background, xenon-based detectors. Each individual PMT will be isolated from the gas by an individual, pressure resistant enclosure and will be coupled to the sensitive volume through a sapphire window. The tracking plane consists in an array of Hamamatsu S10362-11-050P MPPCs used as tracking pixels. They will be arranged in square boards holding 64 sensors (8 times8) with a 1-cm pitch. The inner walls of the TPC, the sapphire windows and the boards holding the MPPCs will be coated with tetraphenyl butadiene (TPB), a wavelength shifter, to improve the light collection.Comment: 32 pages, 22 figures, 5 table