|
|
ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Performance of the ATLAS muon triggers in Run 2. J. Instrum., 15(9), P09015–57pp.
Abstract: The performance of the ATLAS muon trigger system is evaluated with proton-proton (pp) and heavy-ion (HI) collision data collected in Run 2 during 2015-2018 at the Large Hadron Collider. It is primarily evaluated using events containing a pair of muons from the decay of Z bosons to cover the intermediate momentum range between 26 GeV and 100 GeV. Overall, the efficiency of the single-muon triggers is about 68% in the barrel region and 85% in the endcap region. The p(T) range for efficiency determination is extended by using muons from decays of J/psi mesons, W bosons, and top quarks. The performance in HI collision data is measured and shows good agreement with the results obtained in pp collisions. The muon trigger shows uniform and stable performance in good agreement with the prediction of a detailed simulation. Dedicated multi-muon triggers with kinematic selections provide the backbone to beauty, quarkonia, and low-mass physics studies. The design, evolution and performance of these triggers are discussed in detail.
|
|
|
|
Garcia Rivas, I., Fernandez Prieto, A., Vazquez Regueiro, P., Garcia Fernandez, D., Kögler, T., Römer, K., et al. (2025). Performance of a CeBr3 scintillator coupled to a photomultiplier tube with an active voltage divider under high bremsstrahlung fluences. J. Instrum., 20(11), P11016–25pp.
Abstract: Proton therapy lacks a standard method to verify the proton range during the treatments in the clinical routine. In this context, the monitoring of prompt gamma-rays in a coaxial geometry using a compact detector based on a CeBr3 scintillator coupled to a commercial photomultiplier tube (PMT) could lead to the identification of proton range deviations. Such detection system could be easily integrated in every treatment room. Although measuring in this geometry profits from an advantageous solid angle, the detector is also exposed to an extreme gamma-ray rate, of up to 10 Mcps. In this work, we present the first experimental performance evaluation for the proposed detector by irradiating it at very high bremsstrahlung rates at the gamma ELBE facility. Using a customized active voltage divider to supply voltage to the PMT, the detection system was able to sustain a photon rate higher than 12 Mcps without dead time while keeping gain drifts below 15% in the best configuration, and to achieve a sub-nanosecond time resolution.
|
|
|
|
LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Mazorra de Cos, J., Oyanguren, A., et al. (2024). The LHCb Upgrade I. J. Instrum., 19(5), P05065–213pp.
Abstract: The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all -software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all -software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software.
|
|
|
|
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.
|
|
