Diez, S. et al, Bernabeu Verdú, J., Civera, J. V., Garcia, C., Garcia-Argos, C., Lacasta, C., et al. (2014). A double-sided, shield-less stave prototype for the ATLAS Upgrade strip tracker for the High Luminosity LHC. J. Instrum., 9, P03012–16pp.
Abstract: A detailed description of the integration structures for the barrel region of the silicon strips tracker of the ATLAS Phase-II upgrade for the upgrade of the Large Hadron Collider, the so-called High Luminosity LHC (HL-LHC), is presented. This paper focuses on one of the latest demonstrator prototypes recently assembled, with numerous unique features. It consists of a shortened, shield-less, and double sided stave, with two candidate power distributions implemented. Thermal and electrical performances of the prototype are presented, as well as a description of the assembly procedures and tools.
|
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.
|
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.
|
ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fassi, F., Ferrer, A., et al. (2014). Search for WZ resonances in the fully leptonic channel using pp collisions at root s=8 TeV with the ATLAS detector. Phys. Lett. B, 737, 223–243.
Abstract: A search for resonant WZ production in the l nu l'l' (l, l' = e, mu) decay channel using 20.3 fb(-1) of root s = 8 TeV pp collision data collected by the ATLAS experiment at LHC is presented. No significant deviation from the Standard Model prediction is observed and upper limits on the production cross sections of WZ resonances from an extended gauge model W' and from a simplified model of heavy vector triplets are derived. A corresponding observed (expected) lower mass limit of 1.52 (1.49) TeV is derived for the W' at the 95% confidence level. (C) 2014 CERN for the benefit of the ATLAS Collaboration.
|
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.
|
