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Gololo, M. G. D., Carrio Argos, F., & Mellado, B. (2022). Tile Computer-on-Module for the ATLAS Tile Calorimeter Phase-II upgrades. J. Instrum., 17(6), P06020–14pp.
Abstract: The Tile PreProcessor (TilePPr) is the core element of the Tile Calorimeter (TileCal) off-detector electronics for High-luminosity Large Hadron Collider (HL-LHC). The TilePPr comprises FPGA-based boards to operate and read out the TileCal on-detector electronics. The Tile Computer on Module (TileCoM) mezzanine is embedded within TilePPr to carry out three main functionalities. These include remote configuration of on-detector electronics and TilePPr FPGAs, interface the TilePPr with the ATLAS Trigger and Data Acquisition (TDAQ) system, and interfacing the TilePPr with the ATLAS Detector Control System (DCS) by providing monitoring data. The TileCoM is a 10-layer board with a Zynq UltraScale+ ZU2CG for processing data, interface components to integrate with TilePPr and the power supply to be connected to the Advanced Telecommunication Computing Architecture carrier. A CentOS embedded Linux is deployed on the TileCoM to implement the required functionalities for the HL-LHC. In this paper we present the hardware and firmware developments of the TileCoM system in terms of remote programming, interface with ATLAS TDAQ system and DCS system.
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AGATA Collaboration(Crespi, F. C. L. et al), & Gadea, A. (2013). Response of AGATA segmented HPGe detectors to gamma rays up to 15.1 MeV. Nucl. Instrum. Methods Phys. Res. A, 705, 47–54.
Abstract: The response of AGATA segmented HPGe detectors to gamma rays in the energy range 2-15 MeV was measured. The 15.1 MeV gamma rays were produced using the reaction d(B-11,n gamma)C-12 at E-beam=19.1 MeV, while gamma rays between 2 and 9 MeV were produced using an Am-Be-Fe radioactive source. The energy resolution and linearity were studied and the energy-to-pulse-height conversion resulted to be linear within 0.05%.Experimental interaction multiplicity distributions are discussed and compared with the results of Geant4 simulations. It is shown that the application of gamma-ray tracking allows a suppression of background radiation caused by n-capture in Ge nuclei. Finally the Doppler correction for the 15.1 MeV gamma line, performed using the position information extracted with Pulse-shape analysis is discussed.
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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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NEXT Collaboration(Simon, A. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2017). Application and performance of an ML-EM algorithm in NEXT. J. Instrum., 12, P08009–22pp.
Abstract: The goal of the NEXT experiment is the observation of neutrinoless double beta decay in Xe-136 using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.
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ATLAS Collaboration(Aad, G. et al), Akiot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2023). Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3. J. Instrum., 18(11), P11006–38pp.
Abstract: The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH -> b (b) over barb (b) over bar, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.
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