Speaker
Satsuki Nishimura
Description
In conventional analyses of flavor models, the search space of parameters is often restricted to a certain range to optimize the parameters of the theory within a realistic computational time. In this talk, we propose an analytical method utilizing a diffusion model, which is a type of generative artificial intelligence. This strategy can be applied independently of the specific details of the models in contrast to the conventional methods. Through concrete examples, we will discuss how the predictions of flavor models can be evaluated from a bird's-eye view based on the inverse problem approach, where the machine generates various candidates of parameters that reproduce experimental values.
