Programmable integrated front-end for SiPM/PMT PET detectors with continuous scintillating crystal

[EN] AMIC architecture has been introduced in previous works in order to provide a generic and expandable solution for implementing large number of outputs SiPM array/PMT detectors. The underlying idea in AMIC architecture is to calculate the moments of the detected light distribution in an analog fashion. These moments provide information about energy, x/y position, etc. of the light distribution of the detected event. Moreover this means that a small set of signals contains most of the information of the event, thus reducing the number of channels to be acquired. This paper introduces a new front-end device AMIC2GR which implements the AMIC architecture improving the features of the former integrated devices. Higher bandwidth and filtering coefficient precision along with a lower noise allow to apply some detector enhancements. Inhomogeneity among detector elements throughout the array can be reduced. Depth of interaction measurements can be obtained from the light distribution analysis. Also a common trigger signal can be obtained for the whole detector array. Finally AMIC2GR preamplifier stage close to SiPM output signals optimizes signal to noise ratio, which allows to reduce SiPM gain by using lower operating voltages thus reducing dark noiseThis work was supported by Universitat Politecnica de Val ` encia ` through research program PAID06-10-2212.Herrero Bosch, V.; Monzó Ferrer, JM.; Ros García, A.; Aliaga Varea, RJ.; González Martínez, AJ.; Montoliu, C.; Colom Palero, RJ.... (2012). Programmable integrated front-end for SiPM/PMT PET detectors with continuous scintillating crystal. Journal of Instrumentation. 7. https://doi.org/10.1088/1748-0221/7/12/C12021S7Llosa, G., Barrio, J., Cabello, J., Lacasta, C., Oliver, J. F., Rafecas, M., … Piemonte, C. (2011). Development of a PET prototype with continuous LYSO crystals and monolithic SiPM matrices. 2011 IEEE Nuclear Science Symposium Conference Record. doi:10.1109/nssmic.2011.6153684Herrero-Bosch, V., Lerche, C. W., Spaggiari, M., Aliaga-Varea, R., Ferrando-Jodar, N., & Colom-Palero, R. (2011). AMIC: An Expandable Front-End for Gamma-Ray Detectors With Light Distribution Analysis Capabilities. IEEE Transactions on Nuclear Science, 58(4), 1641-1646. doi:10.1109/tns.2011.2152855Lerche, C. W., Benlloch, J. M., Sanchez, F., Pavon, N., Escat, B., Gimenez, E. N., … Martinez, J. (2005). Depth of /spl gamma/-ray interaction within continuous crystals from the width of its scintillation light-distribution. IEEE Transactions on Nuclear Science, 52(3), 560-572. doi:10.1109/tns.2005.851424Lerche, C. W., Herrero-Bosch, V., Spaggiari, M., Mateo-Jimenez, F., Monz-Ferrer, J. M., Colom-Palero, R. J., & Mora-Mas, F. (2010). Fast circuit topology for spatial signal distribution analysis. 2010 17th IEEE-NPSS Real Time Conference. doi:10.1109/rtc.2010.5750391Bult, K., & Geelen, G. J. G. M. (1992). An inherently linear and compact MOST-only current division technique. IEEE Journal of Solid-State Circuits, 27(12), 1730-1735. doi:10.1109/4.17309

Supernova Model Discrimination with Hyper-Kamiokande

[EN] Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations toward a precise reproduction of the explosion mechanism observed in nature.We thank MacKenzie Warren, Ken'ichiro Nakazato, Tomonori Totani, Adam Burrows, David Vartanyan, and Irene Tamborra for access to the supernova models used in this work and for answering various related questions. This work was supported by MEXT Grant-in-Aid for Scientific Research on Innovative Areas titled "Exploration of Particle Physics and Cosmology with Neutrinos" under grant No. 18H05535, 18H05536, and 18H5537. In addition, participation of individual researchers has been further supported by funds from JSPS, Japan; the European Union's Horizon 2020 Research and Innovation Programme H2020 grant Nos. RISE-GA822070-JENNIFER2 2020 and RISEGA872549-SK2HK; SSTF-BA1402-06, NRF grant Nos. 20090083526, NRF-2015R1A2A1A05001869, NRF-2016R1D1A 1A02936965, NRF-2016R1D1A3B02010606, NRF-2017R1 A2B4012757, and NRF-2018R1A6A1A06024970 funded by the Korean government (MSIP); JSPS-RFBR Grant #20-5250010/20 and the Ministry of Science and Higher Education under contract #075-15-2020-778, Russia; Brazilian Funding agencies, CNPq and CAPES; STFC ST/R00031X/2, ST/T002891/1, ST/V002872/1, Consolidated Grants, UKRI MR/S032843/1 and MR/S034102/1, UK. Software: BONSAI.(Smy 2007), sntools. (Migenda et al. 2021), WCSim, 124. matplotlib.(Hunter 2007), NumPy.(van der Walt et al. 2011), SciPy.(Virtanen et al. 2020)Abe, K.; Adrich, P.; Aihara, H.; Akutsu, R.; Alekseev, I.; Ali, A.; Ameli, F.... (2021). Supernova Model Discrimination with Hyper-Kamiokande. The Astrophysical Journal. 916(1):1-17. https://doi.org/10.3847/1538-4357/abf7c4117916

Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air

[EN] Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. In this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm We also see that the reflectance of PIFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.The NEXT Collaboration acknowledges support from the following agencies and institutions: 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-2014-0398 and CEX2018-000867-S, and the Maria de Maeztu Program MDM-2016-0692; the Generalitat Valenciana under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), DE-AC0207CH11359 (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 (USA). DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC2015-18820. JM-A acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. Finally, we thank Brendon Bullard, Paolo Giromini and Neeraj Tata for helpful discussions and assistance with preliminary measurements.Ghosh, S.; Haefner, J.; Martín-Albo, J.; Guenette, R.; Li, X.; Loya Villalpando, A.; Burch, C.... (2020). Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air. Journal of Instrumentation. 15(11):1-17. https://doi.org/10.1088/1748-0221/15/11/P11031S1171511Auger, M., Auty, D. J., Barbeau, P. S., Bartoszek, L., Baussan, E., Beauchamp, E., … Cleveland, B. (2012). The EXO-200 detector, part I: detector design and construction. Journal of Instrumentation, 7(05), P05010-P05010. doi:10.1088/1748-0221/7/05/p05010Martín-Albo, J., Muñoz Vidal, J., Ferrario, P., Nebot-Guinot, M., Gómez-Cadenas, J. J., … Cárcel, S. (2016). Sensitivity of NEXT-100 to neutrinoless double beta decay. Journal of High Energy Physics, 2016(5). doi:10.1007/jhep05(2016)159Rogers, L., Clark, R. A., Jones, B. J. P., McDonald, A. D., Nygren, D. R., Psihas, F., … Azevedo, C. D. . (2018). High voltage insulation and gas absorption of polymers in high pressure argon and xenon gases. Journal of Instrumentation, 13(10), P10002-P10002. doi:10.1088/1748-0221/13/10/p10002Silva, C., Pinto da Cunha, J., Pereira, A., Chepel, V., Lopes, M. I., Solovov, V., & Neves, F. (2010). Reflectance of polytetrafluoroethylene for xenon scintillation light. Journal of Applied Physics, 107(6), 064902. doi:10.1063/1.3318681Haefner, J., Neff, A., Arthurs, M., Batista, E., Morton, D., Okunawo, M., … Lorenzon, W. (2017). Reflectance dependence of polytetrafluoroethylene on thickness for xenon scintillation light. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 856, 86-91. doi:10.1016/j.nima.2017.01.057Kravitz, S., Smith, R. J., Hagaman, L., Bernard, E. P., McKinsey, D. N., Rudd, L., … Sakai, M. (2020). Measurements of angle-resolved reflectivity of PTFE in liquid xenon with IBEX. The European Physical Journal C, 80(3). doi:10.1140/epjc/s10052-020-7800-6Geis, C., Grignon, C., Oberlack, U., García, D. R., & Weitzel, Q. (2017). Optical response of highly reflective film used in the water Cherenkov muon veto of the XENON1T dark matter experiment. Journal of Instrumentation, 12(06), P06017-P06017. doi:10.1088/1748-0221/12/06/p06017Allison, J., Amako, K., Apostolakis, J., Arce, P., Asai, M., Aso, T., … Barrand, G. (2016). Recent developments in Geant4. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 835, 186-225. doi:10.1016/j.nima.2016.06.12

Neutral Bremsstrahlung Emission in Xenon Unveiled

[EN] We present evidence of non-excimer-based secondary scintillation in gaseous xenon, obtained using both the NEXT-White time projection chamber (TPC) and a dedicated setup. Detailed comparison with first-principle calculations allows us to assign this scintillation mechanism to neutral bremsstrahlung (NBrS), a process that is postulated to exist in xenon that has been largely overlooked.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under Advanced Grant No. 339787-NEXT; the European Unions Framework Program for Research and Innovation Horizon 2020 (20142020) under Grant Agreements No. 674896, No. 690575, and No. 740055; the Ministerio de Economa y Competitividad and the Ministerio de Ciencia, Innovacin y Universidades of Spain under Grants No. FIS2014-53371-C04 and No. RTI2018-095979, the Severo Ochoa Program Grants No. SEV-2014-0398 and No. CEX2018-000867-S, and the Mara de Maeztu Program MDM-2016-0692; the Generalitat Valenciana under Grants No. PROMETEO/2016/120 and No. SEJI/2017/011; the Portuguese FCT under Project No. PTDC/FIS-NUC/3933/2021 and under Project No. UIDP/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under Contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), No. DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), No. DE-FG02-13ER42020 (Texas A&M), and No. DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington (USA). D. G.-D. acknowledges Ramon y Cajal program (Spain) under Contract No. RYC- 2015-18820. J. M.-A. acknowledges support from Fundacin Bancaria la Caixa (ID 100010434), Grant No. LCF/BQ/PI19/11690012. We would like to thank Lorenzo Muniz for insightful discussions on the subtleties of electron transport in gases.Henriques, C.; Amedo, P.; Teixeira, JMR.; González-Díaz, D.; Azevedo, C.; Para, A.; Martín-Albo, J.... (2022). Neutral Bremsstrahlung Emission in Xenon Unveiled. Physical Review X. 12(2):021005-1-021028-23. https://doi.org/10.1103/PhysRevX.12.021005021005-1021028-2312

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). (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]

Measurement of the scintillation resolution in liquid xenon and its impact for future segmented calorimeters

We report on a new measurement of the energy resolution that can be attained in liquid xenon when recording only the scintillation light. Our setup is optimised to maximise light collection, and uses state-of-the-art, high-PDE, VUV-sensitive silicon photomultipliers. We find a value of 2.7% +- 0.3% FWHM at 511 keV, a result much better than previous measurements and very close to the Poissonian resolution that we expect in our setup (3.0% +- 0.7% FWHM at 511 keV). Our results are compatible with a null value of the intrinsic energy resolution in xenon, with an upper bound of 1.5% FWHM at 95% CL at 511 keV, to be compared with 3--4% FWHM in the same region found by theoretical estimations which have been standing for the last twenty years. Our work opens new possibilities for apparatus based on liquid xenon and using scintillation only. In particular it suggests that modular scintillation detectors using liquid xenon can be very competitive as building blocks in segmented calorimeters, with applications to nuclear and particle physics as well as Positron Emission Tomography technology

Front-end electronics for the ALICE TPC-detector

The Front-End electronics for the Time Projection Chamber (TPC) for the ALICE experiment consists of 5x105 channels. A single readout channel is comprised of three basic units: a charge sensitive amplifier/shaper with a fast tail cancellation; a 10 bit 10 Msamples/sec low power ADC; a digital ASIC which contains the zero suppression circuit and a multiple-event buffer. Data from a number of channels (4096) are multiplexed into an optical link (DDL) by means of a local custom bus which can support a data throughput of 2 Mbyte/event at a trigger rate of 50 Hz. The construction of a prototype of this electronics is presented in this paper

The ALICE TPC, a large 3-dimensional tracking device with fast readout for ultra-high multiplicity events

The design, construction, and commissioning of the ALICE Time-Projection Chamber (TPC) is described. It is the main device for pattern recognition, tracking, and identification of charged particles in the ALICE experiment at the CERN LHC. The TPC is cylindrical in shape with a volume close to 90 m^3 and is operated in a 0.5 T solenoidal magnetic field parallel to its axis. In this paper we describe in detail the design considerations for this detector for operation in the extreme multiplicity environment of central Pb--Pb collisions at LHC energy. The implementation of the resulting requirements into hardware (field cage, read-out chambers, electronics), infrastructure (gas and cooling system, laser-calibration system), and software led to many technical innovations which are described along with a presentation of all the major components of the detector, as currently realized. We also report on the performance achieved after completion of the first round of stand-alone calibration runs and demonstrate results close to those specified in the TPC Technical Design Report.Comment: 55 pages, 82 figure