Abstract
We use artificial neural networks to parametrize the shape functions in inclusive semileptonic 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 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 distribution is also discussed.
- Received 29 April 2016
DOI:https://doi.org/10.1103/PhysRevD.94.014031
© 2016 American Physical Society