1,259 research outputs found

    Majorana Neutrino Masses from Neutrinoless Double-Beta Decays and Lepton-Number-Violating Meson Decays

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    The Schechter-Valle theorem states that a positive observation of neutrinoless double-beta (0νββ0\nu \beta \beta) decays implies a finite Majorana mass term for neutrinos when any unlikely fine-tuning or cancellation is absent. In this note, we reexamine the quantitative impact of the Schechter-Valle theorem, and find that current experimental lower limits on the half-lives of 0νββ0\nu \beta \beta-decaying nuclei have placed a restrictive upper bound on the Majorana neutrino mass δmνee<7.43×1029 eV|\delta m^{ee}_\nu| < 7.43 \times 10^{-29}~{\rm eV} radiatively generated at the four-loop level. Furthermore, we generalize this quantitative analysis of 0νββ0\nu \beta \beta decays to that of the lepton-number-violating (LNV) meson decays MM++α+βM^- \to {M^\prime}^+ + \ell^-_\alpha + \ell^-_\beta (for α\alpha, β\beta = ee or μ\mu). Given the present upper limits on these rare LNV decays, we have derived the loop-induced Majorana neutrino masses δmνee<9.7×1018 eV|\delta m^{ee}_\nu| < 9.7 \times 10^{-18}~{\rm eV}, δmνeμ<1.6×1015 eV|\delta m^{e\mu}_\nu| < 1.6 \times 10^{-15}~{\rm eV} and δmνμμ<1.0×1012 eV|\delta m^{\mu \mu}_\nu| < 1.0 \times 10^{-12}~{\rm eV} from Kπ++e+eK^- \to \pi^+ + e^- + e^-, Kπ++e+μK^- \to \pi^+ + e^- + \mu^- and Kπ++μ+μK^- \to \pi^+ + \mu^- + \mu^-, respectively. A partial list of radiative neutrino masses from the LNV decays of DD, DsD_s^{} and BB mesons is also given.Comment: 10 pages, 1 figure, clarification added and references updated, Phys. Lett. B in pres

    Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

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    Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method
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