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Gonzalez, P. (2017). A quark model study of strong decays of X(3915). J. Phys. G, 44(7), 075004–13pp.
Abstract: Strong decays of X(3915) are analyzed from two quark model descriptions of X(3915), a conventional one in terms of the Cornell potential and an unconventional one from a generalized screened potential. We conclude that the experimental suppression of the OZI allowed decay X(3915) -> D (D) over bar might be explained in both cases due to the momentum dependence of the decay amplitude. However, the experimental significance of the OZI forbidden decay X(3915) -> omega J/psi could favor an unconventional description.
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Ayala, C., Cvetic, G., & Kogerler, R. (2017). Lattice-motivated holomorphic nearly perturbative QCD. J. Phys. G, 44(7), 075001–30pp.
Abstract: Newer lattice results indicate that, in the Landau gauge at low spacelike momenta, the gluon propagator and the ghost dressing function are finite non-zero. This leads to a definition of the QCD running coupling, in a specific scheme, that goes to zero at low spacelike momenta. We construct a running coupling which fulfills these conditions, and at the same time reproduces to a high precision the perturbative behavior at high momenta. The coupling is constructed in such a way that it reflects qualitatively correctly the holomorphic (analytic) behavior of spacelike observables in the complex plane of the squared momenta, as dictated by the general principles of quantum field theories. Further, we require the coupling to reproduce correctly the nonstrange semihadronic decay rate of tau lepton which is the best measured low-momentum QCD observable with small higher-twist effects. Subsequent application of the Borel sum rules to the V + A spectral functions of tau lepton decays, as measured by OPAL Collaboration, determines the values of the gluon condensate and of the V + A six-dimensional condensate, and reproduces the data to a significantly higher precision than the usual (MS) over bar running coupling.
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Rubio, B., Gelletly, W., Algora, A., Nacher, E., & Tain, J. L. (2017). Beta decay studies with total absorption spectroscopy and the Lucrecia spectrometer at ISOLDE. J. Phys. G, 44(8), 084004–25pp.
Abstract: Here we present the experimental activities carried out at ISOLDE with the total absorption spectrometer Lucrecia, a large 4 pi scintillator detector designed to absorb a full gamma cascade following beta decay. This spectrometer is designed to measure beta-feeding to excited states without the systematic error called Pandemonium. The set up allows the measurement of decays of very short half life. Experimental results from several campaigns, that focus on the determination of the shapes of beta-decaying nuclei by measuring their beta decay strength distributions as a function of excitation energy in the daughter nucleus, are presented.
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Hinarejos, M., Bañuls, M. C., Perez, A., & de Vega, I. (2017). Non-Markovianity and memory of the initial state. J. Phys. A, 50(32), 335301–17pp.
Abstract: We explore in a rigorous manner the intuitive connection between the non-Markovianity of the evolution of an open quantum system and the performance of the system as a quantum memory. Using the paradigmatic case of a two-level open quantum system coupled to a bosonic bath, we compute the recovery fidelity, which measures the best possible performance of the system to store a qubit of information. We deduce that this quantity is connected, but not uniquely determined, by the non-Markovianity, for which we adopt the Breuer-Laine-Piilo measure proposed in Breuer et al (2009 Phys. Rev. Lett. 103 210401). We illustrate our findings with explicit calculations for the case of a structured environment.
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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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