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arXiv · 1604.07598

NNVub: a Neural Network Approach to $B\to X_u \ell \nu$

Paolo Gambino·Kristopher J. Healey·Cristina Mondino
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Abstract

We use artificial neural networks to parameterize the shape functions in inclusive semileptonic $B$ decays without charm. Our approach avoids the adoption of functional form models and allows for a straightforward implementation of all experimental and theoretical constraints on the shape functions. The results are used to extract $|V_{ub}|$ in the GGOU framework and compared with the original GGOU paper and the latest HFAG results, finding good agreement in both cases. The possible impact of future Belle-II data on the $M_X$ distribution is also discussed.

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Paolo Gambino, Kristopher J. Healey, Cristina Mondino. 2016-04-26. NNVub: a Neural Network Approach to $B\to X_u \ell \nu$. https://doi.org/10.1103/physrevd.94.014031

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