Caballero, L., Albiol, F., Corbi Bellot, A., Domingo-Pardo, C., Leganes Nieto, J. L., Agramunt Ros, J., et al. (2018). Gamma-ray imaging system for real-time measurements in nuclear waste characterisation. J. Instrum., 13, P03016–23pp.
Abstract: Acompact, portable and large field-of-viewgamma camera that is able to identify, locate and quantify gamma-ray emitting radioisotopes in real-time has been developed. The device delivers spectroscopic and imaging capabilities that enable its use it in a variety of nuclear waste characterisation scenarios, such as radioactivity monitoring in nuclear power plants and more specifically for the decommissioning of nuclear facilities. The technical development of this apparatus and some examples of its application in field measurements are reported in this article. The performance of the presented gamma-camera is also benchmarked against other conventional techniques.
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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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Aplin, S., Boronat, M., Dannheim, D., Duarte, J., Gaede, F., Ruiz-Jimeno, A., et al. (2013). Forward tracking at the next e(+)e(-) collider part II: experimental challenges and detector design. J. Instrum., 8, T06001–26pp.
Abstract: We present the second in a series of studies into the forward tracking system for a future linear e(+)e(-) collider with a center-of-mass energy in the range from 250 GeV to 3 TeV. In this note a number of specific challenges are investigated, which have caused a degradation of the tracking and vertexing performance in the forward region in previous experiments. We perform a quantitative analysis of the dependence of the tracking performance on detector design parameters and identify several ways to mitigate the performance loss for charged particles emitted at shallow angle.
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DUNE Collaboration(Abi, B. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2020). First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform. J. Instrum., 15(12), P12004–100pp.
Abstract: The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of 7.2 x 6.1 x 7.0 m(3). It is installed at the CERN Neutrino Platform in a specially-constructed beam that delivers charged pions, kaons, protons, muons and electrons with momenta in the range 0.3 GeV/c to 7 GeV/c. Beam line instrumentation provides accurate momentum measurements and particle identification. The ProtoDUNE-SP detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment, and it incorporates full-size components as designed for that module. This paper describes the beam line, the time projection chamber, the photon detectors, the cosmic-ray tagger, the signal processing and particle reconstruction. It presents the first results on ProtoDUNE-SP's performance, including noise and gain measurements, dE/dx calibration for muons, protons, pions and electrons, drift electron lifetime measurements, and photon detector noise, signal sensitivity and time resolution measurements. The measured values meet or exceed the specifications for the DUNE far detector, in several cases by large margins. ProtoDUNE-SP's successful operation starting in 2018 and its production of large samples of high-quality data demonstrate the effectiveness of the single-phase far detector design.
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Guadilla, V., Algora, A., Estienne, M., Fallot, M., Gelletly, W., Porta, A., et al. (2024). First measurements with a new fl-electron detector for spectral shape studies. J. Instrum., 19(2), P02027–21pp.
Abstract: The shape of the electron spectrum emitted in /3 decay carries a wealth of information about nuclear structure and fundamental physics. In spite of that, few dedicated measurements have been made of /3 -spectrum shapes. In this work we present a newly developed detector for /3 electrons based on a telescope concept. A thick plastic scintillator is employed in coincidence with a thin silicon detector. The first measurements employing this detector have been carried out with mono -energetic electrons from the high-energy resolution electron -beam spectrometer at Bordeaux. Here we report on the good reproduction of the experimental spectra of mono -energetic electrons using Monte Carlo simulations. This is a crucial step for future experiments, where a detailed Monte Carlo characterization of the detector is needed to determine the shape of the /3 -electron spectra by deconvolution of the measured spectra with the response function of the detector. A chamber to contain two telescope assemblies has been designed for future /3 -decay experiments at the Ion Guide Isotope Separator On -Line facility in Jyvaskyla, aimed at improving our understanding of reactor antineutrino spectra.
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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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Brzezinski, K., Oliver, J. F., Gillam, J., Rafecas, M., Studen, A., Grkovski, M., et al. (2016). Experimental evaluation of the resolution improvement provided by a silicon PET probe. J. Instrum., 11, P09016–13pp.
Abstract: A high-resolution PET system, which incorporates a silicon detector probe into a conventional PET scanner, has been proposed to obtain increased image quality in a limited region of interest. Detailed simulation studies have previously shown that the additional probe information improves the spatial resolution of the reconstructed image and increases lesion detectability, with no cost to other image quality measures. The current study expands on the previous work by using a laboratory prototype of the silicon PET-probe system to examine the resolution improvement in an experimental setting. Two different versions of the probe prototype were assessed, both consisting of a back-to-back pair of 1-mm thick silicon pad detectors, one arranged in 32 x 16 arrays of 1.4mm x 1.4mm pixels and the other in 40 x 26 arrays of 1.0mm x 1.0mm pixels. Each detector was read out by a set of VATAGP7 ASICs and a custom-designed data acquisition board which allowed trigger and data interfacing with the PET scanner, itself consisting of BGO block detectors segmented into 8 x 6 arrays of 6mm x 12mm x 30mm crystals. Limited-angle probe data was acquired from a group of Na-22 point-like sources in order to observe the maximum resolution achievable using the probe system. Data from a Derenzo-like resolution phantom was acquired, then scaled to obtain similar statistical quality as that of previous simulation studies. In this case, images were reconstructed using measurements of the PET ring alone and with the inclusion of the probe data. Images of the Na-22 source demonstrated a resolution of 1.5mm FWHM in the probe data, the PET ring resolution being approximately 6 mm. Profiles taken through the image of the Derenzo-like phantom showed a clear increase in spatial resolution. Improvements in peak-to-valley ratios of 50% and 38%, in the 4.8mm and 4.0mm phantom features respectively, were observed, while previously unresolvable 3.2mm features were brought to light by the addition of the probe. These results support the possibility of improving the image resolution of a clinical PET scanner using the silicon PET-probe.
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Sorel, M. (2014). Expected performance of an ideal liquid argon neutrino detector with enhanced sensitivity to scintillation light. J. Instrum., 9, P10002–25pp.
Abstract: Scintillation light is used in liquid argon (LAr) neutrino detectors to provide a trigger signal, veto information against cosmic rays, and absolute event timing. In this work, we discuss additional opportunities offered by detectors with enhanced sensitivity to scintillation light, that is with light collection efficiencies of about 10(-3). We focus on two key detector performance indicators for neutrino oscillation physics: calorimetric neutrino energy reconstruction and neutrino/antineutrino separation in a non-magnetized detector. Our results are based on detailed simulations, with neutrino interactions modelled according to the GENIE event generator, while the charge and light responses of a large LAr ideal detector are described by the Geant4 and NEST simulation tools. A neutrino energy resolution as good as 3.3% RMS for 4 GeV electron neutrino charged-current interactions can in principle be obtained in a large detector of this type, by using both charge and light information. By exploiting muon capture in argon and scintillation light information to veto muon decay electrons, we also obtain muon neutrino identification efficiencies of about 50%, and muon antineutrino misidentification rates at the few percent level, for few-GeV neutrino interactions that are fully contained. We argue that the construction of large LAr detectors with sufficiently high light collection efficiencies is in principle possible.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Aguilar, J. A., Bigongiari, C., Calvo Diaz-Aldagalan, D., Emanuele, U., Gomez-Gonzalez, J. P., et al. (2013). Expansion cone for the 3-inch PMTs of the KM3NeT optical modules. J. Instrum., 8, T03006–20pp.
Abstract: Detection of high-energy neutrinos from distant astrophysical sources will open a new window on the Universe. The detection principle exploits the measurement of Cherenkov light emitted by charged particles resulting from neutrino interactions in the matter containing the telescope. A novel multi-PMT digital optical module (DOM) was developed to contain 31 3-inch photomultiplier tubes (PMTs). In order to maximize the detector sensitivity, each PMT will be surrounded by an expansion cone which collects photons that would otherwise miss the photocathode. Results for various angles of incidence with respect to the PMT surface indicate an increase in collection efficiency by 30% on average for angles up to 45 degrees with respect to the perpendicular. Ray-tracing calculations could reproduce the measurements, allowing to estimate an increase in the overall photocathode sensitivity, integrated over all angles of incidence, by 27% (for a single PMT). Prototype DOMs, being built by the KM3NeT consortium, will be equipped with these expansion cones.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
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