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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Andricek, L., Boronat, M., Fuster, J., Garcia, I., Gomis, P., Marinas, C., et al. (2016). Integrated cooling channels in position-sensitive silicon detectors. J. Instrum., 11, P06018–15pp.
Abstract: We present an approach to construct position-sensitive silicon detectors with an integrated cooling circuit. Tests on samples demonstrate that a very modest liquid flow very effectively cool the devices up to a power dissipation of over 10 W/cm(2). The liquid flow is found to have a negligible impact on the mechanical stability. A finite-element simulation predicts the cooling performance to an accuracy of approximately 10%.
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ATLAS Collaboration(Aad, G. et al), Bernabeu Verdu, 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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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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