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AGATA Collaboration, Farnea, E., Recchia, F., Bazzacco, D., Kroll, T., Podolyak, Z., et al. (2010). Conceptual design and Monte Carlo simulations of the AGATA array. Nucl. Instrum. Methods Phys. Res. A, 621(1-3), 331–343.
Abstract: The aim of the Advanced GAmma Tracking Array (AGATA) project is the construction of an array based on the novel concepts of pulse shape analysis and gamma-ray tracking with highly segmented Ge semiconductor detectors. The conceptual design of AGATA and its performance evaluation under different experimental conditions has required the development of a suitable Monte Carlo code. In this article, the description of the code as well as simulation results relevant for AGATA, are presented.
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ANTARES Collaboration(Aguilar, J. A. et al), Bigongiari, C., Dornic, D., Emanuele, U., Gomez-Gonzalez, J. P., Hernandez-Rey, J. J., et al. (2011). AMADEUS-The acoustic neutrino detection test system of the ANTARES deep-sea neutrino telescope. Nucl. Instrum. Methods Phys. Res. A, 626, 128–143.
Abstract: The AMADEUS (ANTARES Modules for the Acoustic Detection Under the Sea) system which is described in this article aims at the investigation of techniques for acoustic detection of neutrinos in the deep sea. It is integrated into the ANTARES neutrino telescope in the Mediterranean Sea. Its acoustic sensors, installed at water depths between 2050 and 2300 m, employ piezo-electric elements for the broad-band recording of signals with frequencies ranging up to 125 kHz. The typical sensitivity of the sensors is around – 145 dB re 1 V/mu Pa (including preamplifier). Completed in May 2008, AMADEUS consists of six “acoustic clusters”, each comprising six acoustic sensors that are arranged at distances of roughly 1 m from each other. Two vertical mechanical structures (so-called lines) of the ANTARES detector host three acoustic clusters each. Spacings between the clusters range from 14.5 to 340 m. Each cluster contains custom-designed electronics boards to amplify and digitise the acoustic signals from the sensors. An on-shore computer cluster is used to process and filter the data stream and store the selected events. The daily volume of recorded data is about 10 GB. The system is operating continuously and automatically, requiring only little human intervention. AMADEUS allows for extensive studies of both transient signals and ambient noise in the deep sea, as well as signal correlations on several length scales and localisation of acoustic point sources. Thus the system is excellently suited to assess the background conditions for the measurement of the bipolar pulses expected to originate from neutrino interactions.
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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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ANTARES Collaboration(Adrian-Martinez, S. et al), Aguilar, J. A., Bigongiari, C., Dornic, D., Emanuele, U., Gomez-Gonzalez, J. P., et al. (2012). The positioning system of the ANTARES Neutrino Telescope. J. Instrum., 7, T08002–20pp.
Abstract: The ANTARES neutrino telescope, located 40km off the coast of Toulon in the Mediterranean Sea at a mooring depth of about 2475m, consists of twelve detection lines equipped typically with 25 storeys. Every storey carries three optical modules that detect Cherenkov light induced by charged secondary particles (typically muons) coming from neutrino interactions. As these lines are flexible structures fixed to the sea bed and held taut by a buoy, sea currents cause the lines to move and the storeys to rotate. The knowledge of the position of the optical modules with a precision better than 10cm is essential for a good reconstruction of particle tracks. In this paper the ANTARES positioning system is described. It consists of an acoustic positioning system, for distance triangulation, and a compass-tiltmeter system, for the measurement of the orientation and inclination of the storeys. Necessary corrections are discussed and the results of the detector alignment procedure are described.
Keywords: Timing detectors; Detector modelling and simulations II (electric fields, charge transport, multiplication and induction, pulse formation, electron emission, etc); Detector alignment and calibration methods (lasers, sources, particle-beams); Detector control systems (detector and experiment monitoring and slow-control systems, architecture, hardware, algorithms, databases)
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ATLAS TRT collaboration(Mindur, B. et al), Mitsou, V. A., & Valls Ferrer, J. A. (2016). Gas gain stabilisation in the ATLAS TRT detector. J. Instrum., 11, P04027–19pp.
Abstract: The ATLAS (one of two general purpose detectors at the LHC) Transition Radiation Tracker (TRT) is the outermost of the three tracking subsystems of the ATLAS Inner Detector. It is a large straw-based detector and contains about 350,000 electronics channels. The performance of the TRT as tracking and particularly particle identification detector strongly depends on stability of the operation parameters with most important parameter being the gas gain which must be kept constant across the detector volume. The gas gain in the straws can vary significantly with atmospheric pressure, temperature, and gas mixture composition changes. This paper presents a concept of the gas gain stabilisation in the TRT and describes in detail the Gas Gain Stabilisation System (GGSS) integrated into the Detector Control System (DCS). Operation stability of the GGSS during Run-1 is demonstrated.
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