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Camarero, D., de Azcarraga, J. A., & Izquierdo, J. M. (2017). Bosonic D=11 supergravity from a generalized Chern-Simons action. Nucl. Phys. B, 923, 633–652.
Abstract: It is shown that the action of the bosonic sector of D= 11supergravity may be obtained by means of a suitable scaling of the originally dimensionless fields of a generalized Chern-Simons action. This follows from the eleven-form CS-potential of the most general linear combination of closed, gauge invariant twelve-forms involving the sp(32)-valued two-form curvatures supplemented by a three-form field. In this construction, the role of the skewsymmetric four-index auxiliary function needed for the first order formulation of D= 11supergravity is played by the gauge field associated with the five Lorentz indices generator of the bosonic sp(32) subalgebra of osp(1|32).
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Becker, R., Buck, A., Casella, C., Dissertori, G., Fischer, J., Howard, A., et al. (2017). The SAFIR experiment: Concept, status and perspectives. Nucl. Instrum. Methods Phys. Res. A, 845, 648–651.
Abstract: The SAFIR development represents a novel Positron Emission Tomography (PET) detector, conceived for preclinical fast acquisitions inside the bore of a Magnetic Resonance Imaging (MRI) scanner. The goal is hybrid and simultaneous PET/MRI dynamic studies at unprecedented temporal resolutions of a few seconds. The detector relies on matrices of scintillating LSO-based crystals coupled one-to-one with SiPM arrays and readout by fast ASIC5 with excellent timing resolution and high rate capabilities. The paper describes the detector concept and the initial results in terms of simulations and characterisation measurements.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2017). Search for heavy resonances decaying to a Z boson and a photon in pp collisions at root s=13 TeV with the ATLAS detector. Phys. Lett. B, 764, 11–30.
Abstract: This Letter presents a search for new resonances with mass larger than 250 GeV, decaying to a Z boson and a photon. The dataset consists of an integrated luminosity of 3.2 fb(-1) of pp collisions collected at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider. The Z bosons are identified through their decays either to charged, light, lepton pairs (e(+) e(-), mu(+) mu(-)) or to hadrons. The data are found to be consistent with the expected background in the whole mass range investigated and upper limits are set on the production cross section times decay branching ratio to Z gamma of a narrow scalar boson with mass between 250 GeV and 2.75 TeV.
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LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., Ruiz Valls, P., et al. (2017). Search for the CP-violating strong decays eta -> pi(+)pi(-) and eta ' (958) -> pi(+)pi(-). Phys. Lett. B, 764, 233–240.
Abstract: A search for the CP-violating strong decays eta -> pi(+)pi(-) and eta ' (958) -> pi(+)pi(-) has been performed using approximately 2.5 x 10(7) events of each of the decays D+ -> pi(+)pi(+)pi(-) and D-s(+) -> pi(+)pi(+)pi(-), recorded by the LHCb experiment. The data set corresponds to an integrated luminosity of 3.0 fb(-1) of pp collision data recorded during LHC Run 1 and 0.3fb(-1) recorded in Run 2. No evidence is seen for D-(s)(+) -> pi(+)eta((')) with eta((')) -> pi(+)pi(-), and upper limits at 90% confidence level are set on the branching fractions, B(eta -> pi(+)pi(-)) < 1.6 x 10(-5) and B(eta' -> pi(+)pi(-)) < 1.8 x 10(-5). The limit for the eta decay is comparable with the existing one, while that for the eta' is a factor of three smaller than the previous limit.
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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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