Speaker
Adrian Antonio Petre
(ETH Zurich (CH))
Description
We introduce a new approach using generative machine learning to sample meaningful generator-level events given reconstructed events in the CMS detector. Our method combines Transformers and Normalizing Flows to tackle the challenge of integrating the Matrix Element Method with importance sampling. We propose using a Transformer network to analyze the full reconstructed event and extract latent information, which is then used to condition a Normalizing Flow network. This approach enables the generation of probable sets of partons that are compatible with observed objects. We demonstrate the performance of our approach on a complex final state, like ttH(bb) in the semileptonic decay channel, and discuss potential applications.
Primary author
Adrian Antonio Petre
(ETH Zurich (CH))
