|
|
Abdallah, J. et al, Carrio, F., Fiorini, L., Garcia Aparisi, F. B., Rodriguez Bosca, S., Valero, A., et al. (2021). Study of energy response and resolution of the ATLAS Tile Calorimeter to hadrons of energies from 16 to 30 GeV. Eur. Phys. J. C, 81(6), 549–18pp.
Abstract: Three spare modules of the ATLAS Tile Calorimeter were exposed to test beams from the Super Proton Synchrotron accelerator at CERN in 2017. The detector's measurements of the energy response and resolution to positive pions and kaons, and protons with energies ranging from 16 to 30 GeV are reported. The results have uncertainties of a few percent. They were compared to the predictions of the Geant4-based simulation program used in ATLAS to estimate the response of the detector to proton-proton events at the Large Hadron Collider. The determinations obtained using experimental and simulated data agree within the uncertainties.
|
|
|
|
Carrio, F. (2022). The Data Acquisition System for the ATLAS Tile Calorimeter Phase-II Upgrade Demonstrator. IEEE Trans. Nucl. Sci., 69(4), 687–695.
Abstract: The tile calorimeter (TileCal) is the central hadronic calorimeter of the ATLAS experiment at the large hadron collider (LHC). In 2025, the LHC will be upgraded leading to the high luminosity LHC (HL-LHC). The HL-LHC will deliver an instantaneous luminosity up to seven times larger than the LHC nominal luminosity. The ATLAS Phase-II upgrade (2025-2027) will accommodate the subdetectors to the HL-LHC requirements. As part of this upgrade, the majority of the TileCal on-detector and off-detector electronics will be replaced using a new readout strategy, where the on-detector electronics will digitize and transmit digitized detector data to the off-detector electronics at the bunch crossing frequency (40 MHz). In the counting rooms, the off-detector electronics will compute reconstructed trigger objects for the first-level trigger and will store the digitized samples in pipelined buffers until the reception of a trigger acceptance signal. The off-detector electronics will also distribute the LHC clock to the on-detector electronics embedded within the digital data stream. The TileCal Phase-II upgrade project has undertaken an extensive research and development program that includes the development of a Demonstrator module to evaluate the performance of the new clock and readout architecture envisaged for the HL-LHC. The Demonstrator module equipped with the latest version of the on-detector electronics was built and inserted into the ATLAS experiment. The Demonstrator module is operated and read out using a Tile PreProcessor (TilePPr) Demonstrator which enables backward compatibility with the present ATLAS Trigger and Data AcQuisition (TDAQ), and the timing, trigger, and command (TTC) systems. This article describes in detail the main hardware and firmware components of the clock distribution and data acquisition systems for the Demonstrator module, focusing on the TilePPr Demonstrator.
|
|
|
|
Carrio, F., Castillo Gimenez, V., Ferrer, A., Gonzalez, V., Higon-Rodriguez, E., Marin, C., et al. (2011). Optical Link Card Design for the Phase II Upgrade of TileCal Experiment. IEEE Trans. Nucl. Sci., 58(4), 1657–1663.
Abstract: This paper presents the design of an optical link card developed in the frame of the R&D activities for the phase 2 upgrade of the TileCal experiment. This board, that is part of the evaluation of different technologies for the final choice in the next years, is designed as a mezzanine that can work independently or be plugged in the optical multiplexer board of the TileCal backend electronics. It includes two SNAP 12 optical connectors able to transmit and receive up to 75 Gb/s and one SFP optical connector for lower speeds and compatibility with existing hardware as the read out driver. All processing is done in a Stratix II GX field-programmable gate array (FPGA). Details are given on the hardware design, including signal and power integrity analysis, needed when working with these high data rates and on firmware development to obtain the best performance of the FPGA signal transceivers and for the use of the GBT protocol.
|
|
|
|
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.
|
|
|
|
Cervello, A., Carrio, F., Garcia, R., Martos, J., Soret, J., Torres, J., et al. (2022). The TileCal PreProcessor interface with the ATLAS global data acquisition system at the HL-LHC. Nucl. Instrum. Methods Phys. Res. A, 1043, 167492–2pp.
Abstract: The Large Hadron Collider (LHC) has envisaged a series of upgrades towards a High Luminosity LHC (HL-LHC) delivering five times the LHC nominal instantaneous luminosity. It will take place throughout 2026-2028, corresponding to the Long Shutdown 3. During this upgrade, the ATLAS Tile Hadronic Calorimeter (TileCal) will replace completely on-and off-detector electronics adopting a new read-out architecture. Signals captured from TileCal are digitized by the on-detector electronics and transmitted to the TileCal PreProcessor (TilePPr) located off-detector, which provides the interface with the ATLAS trigger and data acquisition systems.TilePPr receives, process and transmits the data from the on-detector system and transmits it to the Front -End Link eXchange (FELIX) system. FELIX is the ATLAS common hardware in all the subdetectors designed to act as a data router, receiving and forwarding data to the SoftWare Read-Out Driver (SWROD) computers. FELIX also distributes the Timing, Trigger and Control (TTC) signals to the TilePPr to be propagated to the on-detector electronics. The SWROD is an ATLAS common software solution to perform detector specific data processing, including configuration, calibration, control and monitoring of the partitionIn this contribution we will introduce the new read-out elements for TileCal at the HL-LHC, the intercon-nection between the off-detector electronics and the FELIX system, the configuration and implementation for the test beam campaigns, as well as future developments of the preprocessing and monitoring status of the calorimeter modules through the SWROD infrastructure.
|
|
|
|
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.
|
|
