Speaker
Ahmed Youssef
(University of Cincinnati)
Description
Hadronization, a crucial component of event generation, is traditionally simulated using finely-tuned empirical models. While current phenomenological models have achieved significant success in simulating this process, there remain areas where they fall short in accurately describing the underlying physics. In this talk, I will introduce MLHAD, an alternative approach that supplants the empirical model with a surrogate machine learning-based method, thereby facilitating data-trainability. I will delve into the current stage of its development and explore potential future direction.
Author
Ahmed Youssef
(University of Cincinnati)
