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May 11, 2020 | 10:00 a.m. - 11:00 a.m.
Category: Seminar
Location: Online
Cost: Free
Audience: Alumni, Community, Current Graduate Students, Current Undergraduate Students, Faculty

The radiative decay $bar{B}to X_sgamma$ and semileptonic heavy meson decay $Dto pi l nu$ are important flavor physics probes of new physics. However, these decays are plagued with nonperturbative uncertainties that are needed to be controlled to obtain a theoretically clean description. In this dissertation, we provide effective field theory and machine learning approaches to controlling these uncertainties.

In $bar Bto X_sgamma$, the largest uncertainty on the total rate arises from $Q_1-Q_{7gamma}$ operator pair. This contribution is given by a soft function whose moments are related to nonperturbative heavy quark effective theory (HQET) operators' matrix elements. The extraction of higher-order moments requires the knowledge of higher dimensional HQET operators. We present a general method that allows for an easy construction of HQET and non-relativistic quantum-chromo dynamics (NRQCD) operators containing emph{any} number of covariant derivatives. As an application, we list, for the first time, all operators in the dimension eight NRQCD Lagrangian. Then we use recently extracted HQET matrix elements to reevaluate the nonperturbative uncertainty of $bar Bto X_sgamma$ total decay rate and CP asymmetry.

The decay rate of semileptonic $Dto pi l nu$ is proportional to the hadronic form factors. Currently, these form factors cannot be determined analytically in the whole range of available momentum transfer $q^2$, but can be parameterized with a varying degree of model dependency. We propose a machine learning approach with artificial neural networks trained from experimental pseudo-data to predict the shape of these form factors with a prescribed uncertainty. This provides the first model-independent parameterization of $Dto pi l nu$ vector form factor shape in the literature.

For more information about this event, please contact Gil Paz at 313-577-2756 or gilpaz@wayne.edu.