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Barrientos, D., Bellato, M., Bazzacco, D., Bortolato, D., Cocconi, P., Gadea, A., et al. (2015). Performance of the Fully Digital FPGA-Based Front-End Electronics for the GALILEO Array. IEEE Trans. Nucl. Sci., 62(6), 3134–3139.
Abstract: In this work we present the architecture and results of a fully digital Front End Electronics (FEE) read out system developed for the GALILEO array. The FEE system, developed in collaboration with the Advanced Gamma Tracking Array (AGATA) collaboration, is composed of three main blocks: preamplifiers, digitizers and preprocessing electronics. The slow control system contains a custom Linux driver, a dynamic library and a server implementing network services. This work presents the first results of the digital FEE system coupled with a GALILEO germanium detector, which has demonstrated the capability to achieve an energy resolution of 1.53% at an energy of 1.33 MeV, similar to the one obtained with a conventional analog system. While keeping a good performance in terms of energy resolution, digital electronics will allow to instrument the full GALILEO array with a versatile system with high integration and low power consumption and costs.
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Labiche, M. et al, Caballero, L., & Rubio, B. (2010). TIARA: A large solid angle silicon array for direct reaction studies with radioactive beams. Nucl. Instrum. Methods Phys. Res. A, 614(3), 439–448.
Abstract: A compact, quasi-4 pi position sensitive silicon array. TIARA, designed to study direct reactions induced by radioactive beams in inverse kinematics is described here. The Transfer and Inelastic All-angle Reaction Array (TIARA) consists of 8 resistive charge division detectors forming an octagonal barrel around the target and a set of double-sided silicon-strip annular detectors positioned at each end of the barrel. The detector was coupled to the gamma-ray array EXOGAM and the spectrometer VAMOS at the GANIL Laboratory to demonstrate the potential of such an apparatus with radioactive beams. The N-14(d,p)N-15 reaction, well known in direct kinematics, has been carried out in inverse kinematics for that purpose. The observation of the N-15 ground state and excited states at 7.16 and 7.86 MeV is presented here as well as the comparison of the measured proton angular distributions with DWBA calculations. Transferred l-values are in very good agreement with both theoretical calculations and previous experimental results obtained in direct kinematics.
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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2015). Identification of beauty and charm quark jets at LHCb. J. Instrum., 10, P06013–29pp.
Abstract: Identification of jets originating from beauty and charm quarks is important for measuring Standard Model processes and for searching for new physics. The performance of algorithms developed to select b- and c-quark jets is measured using data recorded by LHCb from proton-proton collisions at root s = 7TeV in 2011 and at root s = 8TeV in 2012. The efficiency for identifying a b (c) jet is about 65%(25%) with a probability for misidentifying a light-parton jet of 0.3% for jets with transverse momentum pT > 20GeV and pseudorapidity 2 : 2 < eta < 4.2. The dependence of the performance on the pT and eta of the jet is also measured.
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ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Ferrer, A., Fiorini, L., et al. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. J. Instrum., 9, P09009–34pp.
Abstract: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
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ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fassi, F., Ferrer, A., et al. (2014). Monitoring and data quality assessment of the ATLAS liquid argon calorimeter. J. Instrum., 9, P07024–55pp.
Abstract: The liquid argon calorimeter is a key component of the ATLAS detector installed at the CERN Large Hadron Collider. The primary purpose of this calorimeter is the measurement of electron and photon kinematic properties. It also provides a crucial input for measuring jets and missing transverse momentum. An advanced data monitoring procedure was designed to quickly identify issues that would affect detector performance and ensure that only the best quality data are used for physics analysis. This article presents the validation procedure developed during the 2011 and 2012 LHC data-taking periods, in which more than 98% of the proton-proton luminosity recorded by ATLAS at a centre-of-mass energy of 7-8 TeV had calorimeter data quality suitable for physics analysis.
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