| Abstract: |
However, a large-scale scan of high dimensional parameter space under vast experimental constraints is typically a time-consuming and expensive task. In this talk, a new self-learning scan approach, named Machine Learning Scan (MLS), is introduced. This MLS can achieve a fast and reliable exploration of high dimensional parameter space by using machine learning models to evaluate the quality of random parameter sets. As a proof-of-concept, we apply MLS to several benchmark models, including the alignment limit of the minimal supersymmetric model, and find that such a method can significantly reduce the computational cost and ensure the discovery of all survived regions (comparisons with the conventional scan methods are provided). |
