Neural network approach to BXuν

Paolo Gambino, Kristopher J. Healey, and Cristina Mondino
Phys. Rev. D 94, 014031 – Published 26 July 2016

Abstract

We use artificial neural networks to parametrize 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 |Vub| 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 MX distribution is also discussed.

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  • Received 29 April 2016

DOI:https://doi.org/10.1103/PhysRevD.94.014031

© 2016 American Physical Society

Physics Subject Headings (PhySH)

Particles & Fields

Authors & Affiliations

Paolo Gambino1, Kristopher J. Healey1, and Cristina Mondino1,2

  • 1Dip. di Fisica, Università di Torino & INFN, Torino, 10125 Torino, Italy
  • 2Center for Cosmology and Particle Physics, Physics Department, New York University, New York, New York 10003, USA

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Issue

Vol. 94, Iss. 1 — 1 July 2016

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