Carrio, F., Kim, H. Y., Moreno, P., Reed, R., Sandrock, C., Schettino, V., et al. (2014). Design of an FPGA-based embedded system for the ATLAS Tile Calorimeter front-end electronics test-bench. J. Instrum., 9, C03023–12pp.
Abstract: The portable test-bench for the certification of the ATLAS tile hadronic calorimeter front-end electronics has been redesigned for the present Long Shutdown (LS1) of LHC, improving its portability and expanding its functionalities. This paper presents a new test-bench based on a Xilinx Virtex-5 FPGA that implements an embedded system using a PowerPC 440 microprocessor hard core and custom IP cores. A light Linux version runs on the PowerPC microprocessor and handles the IP cores which implement the different functionalities needed to perform the desired tests such as TTCvi emulation, G-Link decoding, ADC control and data reception.
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Kalliokoski, M., Levi, G., Maulik, A., Ostrovskiy, I., Patrizii, L., Pinfold, J., et al. (2025). Calibration of Solid State Nuclear Track Detectors for rare event searches. J. Instrum., 20(3), P03014–12pp.
Abstract: The calibration of the CR39 (R) and Makrofol (R) Nuclear Track Detectors of the MoEDAL experiment at the CERN-LHC was performed by exposing stacks of detector foils to heavy ion beams with energies ranging from 340 MeV/nucleon to 150 GeV/nucleon. After chemical etching, the base areas and lengths of etch-pit cones were measured using automatic and manual optical microscopes. The response of the detectors as measured by the ratio of the track-etching rate over the bulk-etching rate, was determined over a range extending from their threshold at Z/beta 7 and 50 for CR39 and Makrofol, respectively, up to Z/beta 92.
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ATLAS Tile Calorimeter System(Abdallah, J. et al), Ferrer, A., Fiorini, L., Hernandez Jimenez, Y., Higon-Rodriguez, E., Ruiz-Martinez, A., et al. (2016). The Laser calibration of the ATLAS Tile Calorimeter during the LHC run 1. J. Instrum., 11, T10005–29pp.
Abstract: This article describes the Laser calibration system of the ATLAS hadronic Tile Calorimeter that has been used during the run 1 of the LHC. First, the stability of the system associated readout electronics is studied. It is found to be stable with variations smaller than 0.6 %. Then, the method developed to compute the calibration constants, to correct for the variations of the gain of the calorimeter photomultipliers, is described. These constants were determined with a statistical uncertainty of 0.3 % and a systematic uncertainty of 0.2 % for the central part of the calorimeter and 0.5 % for the end-caps. Finally, the detection and correction of timing mis-configuration of the Tile Calorimeter using the Laser system are also presented.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2024). Helium identification with LHCb. J. Instrum., 19(2), P02010–23pp.
Abstract: The identification of helium nuclei at LHCb is achieved using a method based on measurements of ionisation losses in the silicon sensors and timing measurements in the Outer Tracker drift tubes. The background from photon conversions is reduced using the RICH detectors and an isolation requirement. The method is developed using pp collision data at root s = 13 TeV recorded by the LHCb experiment in the years 2016 to 2018, corresponding to an integrated luminosity of 5.5 fb(-1). A total of around 10(5) helium and antihelium candidates are identified with negligible background contamination. The helium identification efficiency is estimated to be approximately 50% with a corresponding background rejection rate of up to O(10(12)). These results demonstrate the feasibility of a rich programme of measurements of QCD and astrophysics interest involving light nuclei.
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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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