Speaker
Description
In this talk, we will explore the newest developments and user contributions of zfit, a cutting-edge Python library designed for fitting binned and unbinned likelihoods within the High Energy Physics (HEP) analysis ecosystem. Built on TensorFlow, zfit offers a high-level interface for defining and fitting models, facilitating efficient and robust analysis workflows.
Over the past five years, zfit has evolved significantly, achieving a stable core and comprehensive feature set. Recent updates have increased the compatibility with the Python ecosystem for binned and unbinned fits and greatly improved the user-friendliness through various features. zfit-physics, an extension to zfit that provides physics specific models, has been largely extended thanks to a low-entry barrier for user contributions. We will show-case some of the new PDFs and outline how the community can create their own PDFs and make them available to a wide audience by contributing to zfit -- thereby shaping zfit's future trajectory.
