Speaker
Kinga Anna Wozniak
(Universite de Geneve (CH))
Description
In this talk, we address the challenge of optimizing detector design for advanced tasks in high energy particle physics. Our goal is to develop differentiable pipelines for the optimization of typical metrics sought out in particle physics applications. The approach is tailored with a focus on several critical design aspects, including optimizing detector performance, enhancing sensitivity to discoveries, and adhering to cost constraints. We explore innovative machine learning-based methods leveraging mutual information and generative AI. We present initial promising results from studies utilizing both methodologies and discuss potential directions for further development.
Authors
Atul Sinha
François Fleuret
Jan Kieseler
(KIT - Karlsruhe Institute of Technology (DE))
Kinga Anna Wozniak
(Universite de Geneve (CH))
Stephen Mulligan
(Universite de Genève)
Tobias Golling
(Universite de Geneve (CH))
