Speaker
Description
Model fitting using likelihoods is a crucial part of many analyses in HEP.
zfit started over five years ago with the goal of providing this capability within the Python analysis ecosystem by offering a variety of advanced features and high performance tailored to the needs of HEP.
After numerous iterations with users and a continuous development, zfit reached a maturity stage with a stable core and feature set.
In this talk, we will highlight the latest developments. We will discuss its comprehensive feature set, which includes binned and unbinned fits, advanced model building and the ability to create custom models and a variety of available minimizers. Additionally, the talk will cover current and future backend strategies, leveraging TensorFlow and JAX, to deliver state-of-the-art performance on both CPUs and GPUs through extensive optimizations. Furthermore, we will explore the seamless integration of zfit into the broader Python HEP ecosystem, primarily with Scikit-HEP libraries, and its capability to serialize likelihoods in a human-readable format.
