Zfit

Zfit is a Python library designed for performing fits to data, particularly in the context of high-energy physics. It builds upon the ROOT framework, a data analysis toolkit widely used in scientific research, and provides a more Pythonic and user-friendly interface for common fitting tasks. The primary goal of Zfit is to enable physicists to create and fit complex models to their experimental data efficiently and reliably.

The library offers a flexible way to define probability distributions and models. Users can construct models by combining basic building blocks, such as simple shapes like Gaussian or exponential functions, or more intricate custom-defined functions. This modular approach allows for the creation of sophisticated models that represent theoretical predictions or empirical descriptions of physical processes. Zfit supports various fitting algorithms, including maximum likelihood estimation, which is a standard technique for parameter estimation in particle physics.

A key feature of Zfit is its focus on supporting multi-dimensional fits. This is crucial for analyzing data with many variables, allowing researchers to explore correlations and dependencies between different observable quantities. The library is designed to be numerically stable and efficient, handling large datasets and complex models without significant performance degradation. Its integration with Python's scientific ecosystem, including libraries like NumPy and SciPy, further enhances its utility for data analysis and visualization.