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Effective Field Theory And Machine Learning Approaches To Controlling Nonperturbative Uncertainties In Flavor Physics, Ayesh Gunawardana
Effective Field Theory And Machine Learning Approaches To Controlling Nonperturbative Uncertainties In Flavor Physics, Ayesh Gunawardana
Wayne State University Dissertations
The radiative decay $\bar{B}\to X_s\gamma$ and semileptonic heavy meson decay $D\to \pi l \nu$ 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.\par
In $\bar B\to X_s\gamma$, the largest uncertainty on the total rate and the CP asymmetry arises from resolved photon contributions. These appear first at order $1/m_b$ and are related to operators other than $Q_{7\gamma}$ in the effective weak Hamiltonian. One of the …