ANTARES, I. C., Pierre Auger, LIGO Sci and VIRGO Collaborations(Albert, A. et al), Barrios-Marti, J., Coleiro, A., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2017). Search for High-energy Neutrinos from Binary Neutron Star Merger GW170817 with ANTARES, IceCube, and the Pierre Auger Observatory. Astrophys. J. Lett., 850(2), L35–18pp.
Abstract: The Advanced LIGO and Advanced Virgo observatories recently discovered gravitational waves from a binary neutron star inspiral. A short gamma-ray burst (GRB) that followed the merger of this binary was also recorded by the Fermi Gamma-ray Burst Monitor (Fermi-GBM), and the Anti-Coincidence Shield for the Spectrometer for the International Gamma-Ray Astrophysics Laboratory (INTEGRAL), indicating particle acceleration by the source. The precise location of the event was determined by optical detections of emission following the merger. We searched for high-energy neutrinos from the merger in the GeV-EeV energy range using the ANTARES, IceCube, and Pierre Auger Observatories. No neutrinos directionally coincident with the source were detected within +/- 500 s around the merger time. Additionally, no MeV neutrino burst signal was detected coincident with the merger. We further carried out an extended search in the direction of the source for high-energy neutrinos within the 14 day period following the merger, but found no evidence of emission. We used these results to probe dissipation mechanisms in relativistic outflows driven by the binary neutron star merger. The non-detection is consistent with model predictions of short GRBs observed at a large off-axis angle.
|
Korichi, A., Lauritsen, T., Wilson, A. N., Dudouet, J., Clement, E., Lalovic, N., et al. (2017). Performance of a gamma-ray tracking array: Characterizing the AGATA array using a Co-60 source. Nucl. Instrum. Methods Phys. Res. A, 872, 80–86.
Abstract: The AGATA (Advanced GAmma Tracking Array) tracking detector is being designed to far surpass the performance of the previous generation, Compton-suppressed arrays. In this paper, a characterization of AGATA is provided based on data from the second GSI campaign. Emphasis is placed on the proper corrections required to extract the absolute photopeak efficiency and peak-to-total ratio. The performance after tracking is extracted and GEANT4 simulations are used both to understand the results and to scale the measurements up to predicted values for the full 4 pi implementation of the device.
|
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.
|
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.
|
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.
|
