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Esperante-Pereira, D. (2014). DEPFET active pixel sensors for the vertex detector of the Belle-II experiment. J. Instrum., 9, C03004–11pp.
Abstract: Active pixels sensors based on the DEPFET technology will be used for the innermost vertex detector of the future Belle-II experiment. The increased luminosity of the e(+) e(-) SuperKEKB collider entails challenging detector requirements, namely: low material budget, low power consumption, high precision and efficiency, and a large readout rate. The DEPFET active pixel technology has shown to be a suitable solution for this purpose. A review of the different aspects of the detector design (sensors, readout ASICS and supplementary infrastructure) and the results of the latest thinned sensor prototypes (50 μm) are described.
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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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ATLAS Collaboration(Aad, G. et al), Bernabeu Verdú, J., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fassi, F., et al. (2014). Operation and performance of the ATLAS semiconductor tracker. J. Instrum., 9, P08009–73pp.
Abstract: The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74 +/- 0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, delta-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations.
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Rebel, B., Hall, C., Bernard, E., Faham, C. H., Ito, T. M., Lundberg, B., et al. (2014). High voltage in noble liquids for high energy physics. J. Instrum., 9, T08004–57pp.
Abstract: A workshop was held at Fermilab November 8-9, 2013 to discuss the challenges of using high voltage in noble liquids. The participants spanned the fields of neutrino, dark matter, and electric dipole moment physics. All presentations at the workshop were made in plenary sessions. This document summarizes the experiences and lessons learned from experiments in these fields at developing high voltage systems in noble liquids.
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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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