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Mengoni, D., Duenas, J. A., Assie, M., Boiano, C., John, P. R., Aliaga, R. J., et al. (2014). Digital pulse-shape analysis with a TRACE early silicon prototype. Nucl. Instrum. Methods Phys. Res. A, 764, 241–246.
Abstract: A highly segmented silicon-pad detector prototype has been tested to explore the performance of the digital pulse shape analysis in the discrimination of the particles reaching the silicon detector. For the first time a 200 tun thin silicon detector, grown using an ordinary floating zone technique, has been shown to exhibit a level discrimination thanks to the fine segmentation. Light-charged particles down to few MeV have been separated, including their punch-through. A coaxial HPGe detector in time coincidence has further confirmed the quality of the particle discrimination.
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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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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., King, M., Mitsou, V. A., Vento, V., et al. (2014). The physics programme of the MoEDAL experiment at the LHC. Int. J. Mod. Phys. A, 29(23), 1430050–91pp.
Abstract: The MoEDAL experiment at Point 8 of the LHC ring is the seventh and newest LHC experiment. It is dedicated to the search for highly-ionizing particle avatars of physics beyond the Standard Model, extending significantly the discovery horizon of the LHC. A MoEDAL discovery would have revolutionary implications for our fundamental understanding of the Microcosm. MoEDAL is an unconventional and largely passive LHC detector comprised of the largest array of Nuclear Track Detector stacks ever deployed at an accelerator, surrounding the intersection region at Point 8 on the LHC ring. Another novel feature is the use of paramagnetic trapping volumes to capture both electrically and magnetically charged highly-ionizing particles predicted in new physics scenarios. It includes an array of TimePix pixel devices for monitoring highly-ionizing particle backgrounds. The main passive elements of the MoEDAL detector do not require a trigger system, electronic readout, or online computerized data acquisition. The aim of this paper is to give an overview of the MoEDAL physics reach, which is largely complementary to the programs of the large multipurpose LHC detectors ATLAS and CMS.
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Pallis, C. (2014). Linking Starobinsky-type inflation in no-scale supergravity to MSSM. J. Cosmol. Astropart. Phys., 04(4), 024–31pp.
Abstract: A novel realization of the Starobinsky inflationary model within a moderate extension of the Minimal Supersymmetric Standard Model (MSSM) is presented. The proposed superpotential is uniquely determined by applying a continuous R and a Z2 discrete symmetry, whereas the Kahler potential is associated with a no-scale-type SU(54, 1)/ SU(54) x U(1) R X Z2 Kahler manifold. The inflaton is identified with a Higgs-like modulus whose the vacuum expectation value controls the gravitational strength. Thanks to a strong enough coupling (with a parameter CT involved) between the inflaton and the Ricci scalar curvature, inflation can be attained even for subplanckian values of the inflaton with CT >= 76 and the corresponding effective theory being valid up to the Planck scale. The inflationary observables turn out to be in agreement with the current data and the inflaton mass is predicted to be 3 10(3) GeV. At the cost of a relatively small superpotential coupling constant, the model offers also a resolution of the f,t problem of MSSM for CT <= 4500 and gravitino heavier than about 10(4) GeV. Supplementing MSSM by three right-handed neutrinos we show that spontaneously arising couplings between the inflaton and the particle content of MSSM not only ensure a sufficiently low reheating temperature but also support a scenario of non-thermal leptogenesis consistently with the neutrino oscillation parameters.
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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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