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

An improved measurement of electron-ion recombination in high-pressure xenon gas

We report on results obtained with the NEXT-DEMO prototype of the NEXT-100 high-pressure xenon gas time projection chamber (TPC), exposed to an alpha decay calibration source. Compared to our previous measurements with alpha particles, an upgraded detector and improved analysis techniques have been used. We measure event-by-event correlated fluctuations between ionization and scintillation due to electron-ion recombination in the gas, with correlation coeffcients between -0.80 and -0.56 depending on the drift field conditions. By combining the two signals, we obtain a 2.8% FWHM energy resolution for 5.49 MeV alpha particles and a measurement of the optical gain of the electroluminescent TPC. The improved energy resolution also allows us to measure the specific activity of the radon in the gas due to natural impurities. Finally, we measure the average ratio of excited to ionized atoms produced in the xenon gas by alpha particles to be 0:561 0:045, translating into an average energy to produce a primary scintillation photon ofWex = (39:2 3:2) eV.This work was supported by the following agencies and institutions: the European Research Council under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain under grants CONSOLIDER-Ingenio 2010 CSD2008-0037 (CUP), FPA2009-13697-C04 and FIS2012-37947-C04; the Director, Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under contract no. DE-AC02-05CH11231; and the Portuguese FCT and FEDER through the program COMPETE, project PTDC/FIS/103860/2008.Serra, L.; Sorel, M.; Alvarez, V.; Borges, FIG.; Camargo, M.; Carcel, S.; Cebrian, S.... (2015). An improved measurement of electron-ion recombination in high-pressure xenon gas. Journal of Instrumentation. 10:1-19. https://doi.org/10.1088/1748-0221/10/03/P03025S1191

Background rejection in NEXT using deep neural networks

[EN] We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain and FEDER under grants CONSOLIDER-Ingenio 2010 CSD2008-0037 (CUP), FIS2014-53371-C04 and the Severo Ochoa Program SEV-2014-0398; GVA under grant PROMETEO/2016/120. Fermilab is operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy. JR acknowledges support from a Fulbright Junior Research Award.Renner, J.; Farbin, A.; Muñoz Vidal, J.; Benlloch-Rodríguez, J.; Botas, A.; Ferrario, P.; Gómez-Cadenas, J.... (2017). Background rejection in NEXT using deep neural networks. Journal of Instrumentation. 12. https://doi.org/10.1088/1748-0221/12/01/T01004S1

Ionization and scintillation of nuclear recoils in gaseous xenon

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 α-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.This work was supported by the following agencies and institutions: the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy, and the National Energy Research Scientific Computing Center (NERSC), supported by the Office of Science of the U.S. Department of Energy, both under Contract no. DE-AC02-05CH11231; the European Research Council under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain under Grants CONSOLIDER-Ingenio 2010 C5D2008-0037 (CUP), FPA2009-13697-004-04, FPA2009-13697-C04-01, FIS2012-37947-C04-01, FIS2012-37947-C04-02, FIS2012-37947-C04-03, and FIS2012-37947-C04-04; and the Portuguese FCT and FEDER through the program COMPETE, Projects PTDC/FIS/103860/2008 and PTDC/FIS/112272/2009. J. Renner acknowledges the support of a Department of Energy National Nuclear Security Administration Stewardship Science Graduate Fellowship, grant number DE-FC52-08NA28752.Renner, J.; Gehman, VM.; Goldschmidt, A.; Matis, HS.; Miller, T.; Nakajima, Y.; Nygren, D.... (2015). Ionization and scintillation of nuclear recoils in gaseous xenon. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 793:62-74. https://doi.org/10.1016/j.nima.2015.04.057S627479

Fabricación de recubrimientos de alta resistencia por LMD para estampación en caliente

EUSKERA: Beroko estanpazio prozesuak txapa konformaketaren fabrikazio teknologia da, deformatu beharreko materiala aurreberotu ostean pieza konformatu eta tenplatu egiten da; non trokelaren gune aktiboak higadura latzak jasaten dituen. Balio gehigarri altuko trokel hauen bizi zikloa luzatzeko, takoak kolpeak jasateko ijeztutako altzairu harikor batekin fabrikatzea proposatzen da, gainetik laser prozesu gehigarriaren bidez (LMD) errendimendu altuko estaldura bat gehituko zaio bere gogortasunari esker abrasio bidezko higadura minimizatzen dituena. Horretarako gradiente funtzional (FGM) bidezko estaldura egitea planteatzen da, material ezberdinak konbinatuz, piezaren konposizioa aldatu ahala barne propietateak aldatuko dituena.CASTELLANO: El proceso de estampación en caliente es una tecnología de fabricación de conformado de chapas, en los que se calienta previamente el material a deformar y se procede al conformado y temple de la pieza; donde la parte activa de los troqueles sufre un gran desgaste. Para aumentar la vida de estos útiles de alto valor añadido, se propone fabricar los tacos con un material base de acero laminado dúctil que pueda resistir los continuos impactos de la estampación, sobre el que se aportará mediante aporte por láser (LMD) un recubrimiento de alto rendimiento que gracias a su alta dureza minimice el desgaste por abrasión. Para ello se plantea realizar un recubrimiento con gradiente funcional (FGM), combinando distintos materiales, dotando así a la pieza de distintas propiedades a medida que varía la composición.ENGLISH: The hot stamping process is a manufacturing technology for sheet metal forming, in which the material to be deformed is heated beforehand and the part is formed and hardened; where the active part of the dies suffers a great deal of wear. In order to increase the life of these high added value tools, it is proposed to manufacture the blocks with a ductile laminated steel base material that can resist the continuous impacts of the stamping, on which a high performance coating will be provided by means of a laser contribution (LMD) which, thanks to its high hardness, minimizes wear by abrasion. For this purpose, a coating with a functional gradient (FGM) is proposed, combining different materials, thus providing the part with different properties as the composition varies

Background rejection in NEXT using deep neural networks

We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvemen