NEXT Collaboration(Renner, J. et al), Martinez-Lema, G., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2018). Initial results on energy resolution of the NEXT-White detector. J. Instrum., 13, P10020–14pp.
Abstract: One of the major goals of the NEXT-White (NEW) detector is to demonstrate the energy resolution that an electroluminescent high pressure xenon TPC can achieve for high energy tracks. For this purpose, energy calibrations with Cs-137 and Th-232 sources have been carried out as a part of the long run taken with the detector during most of 2017. This paper describes the initial results obtained with those calibrations, showing excellent linearity and an energy resolution that extrapolates to approximately 1% FWHM at Q(beta beta).
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., et al. (2018). Comparison between simulated and observed LHC beam backgrounds in the ATLAS experiment at E-beam=4 TeV. J. Instrum., 13, P12006–41pp.
Abstract: Results of dedicated Monte Carlo simulations of beam-induced background (BIB) in the ATLAS experiment at the Large Hadron Collider (LHC) are presented and compared with data recorded in 2012. During normal physics operation this background arises mainly from scattering of the 4 TeV protons on residual gas in the beam pipe. Methods of reconstructing the BIB signals in the ATLAS detector, developed and implemented in the simulation chain based on the FLUKA Monte Carlo simulation package, are described. The interaction rates are determined from the residual gas pressure distribution in the LHC ring in order to set an absolute scale on the predicted rates of BIB so that they can be compared quantitatively with data. Through these comparisons the origins of the BIB leading to different observables in the ATLAS detectors are analysed. The level of agreement between simulation results and BIB measurements by ATLAS in 2012 demonstrates that a good understanding of the origin of BIB has been reached.
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NEXT Collaboration(Monrabal, F. et al), Laing, A., Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., et al. (2018). The NEXT White (NEW) detector. J. Instrum., 13, P12010–38pp.
Abstract: Conceived to host 5 kg of xenon at a pressure of 15 bar in the fiducial volume, the NEXT-White apparatus is currently the largest high pressure xenon gas TPC using electroluminescent amplification in the world. It is also a 1:2 scale model of the NEXT-100 detector for Xe-136 beta beta 0 nu decay searches, scheduled to start operations in 2019. Both detectors measure the energy of the event using a plane of photomultipliers located behind a transparent cathode. They can also reconstruct the trajectories of charged tracks in the dense gas of the TPC with the help of a plane of silicon photomultipliers located behind the anode. A sophisticated gas system, common to both detectors, allows the high gas purity needed to guarantee a long electron lifetime. NEXT-White has been operating since October 2016 at the Laboratorio Subterraneo de Canfranc (LSC), in Spain. This paper describes the detector and associated infrastructures, as well as the main aspects of its initial operation.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Resolution of the ATLAS muon spectrometer monitored drift tubes in LHC Run 2. J. Instrum., 14, P09011–35pp.
Abstract: The momentum measurement capability of the ATLAS muon spectrometer relies fundamentally on the intrinsic single-hit spatial resolution of the monitored drift tube precision tracking chambers. Optimal resolution is achieved with a dedicated calibration program that addresses the specific operating conditions of the 354 000 high-pressure drift tubes in the spectrometer. The calibrations consist of a set of timing offsets and drift time to drift distance transfer relations, and result in chamber resolution functions. This paper describes novel algorithms to obtain precision calibrations from data collected by ATLAS in LHC Run 2 and from a gas monitoring chamber, deployed in a dedicated gas facility. The algorithm output consists of a pair of correction constants per chamber which are applied to baseline calibrations, and determined to be valid for the entire ATLAS Run 2. The final single-hit spatial resolution, averaged over 1172 monitored drift tube chambers, is 81.7 +/- 2.2 μm.
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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
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