zfit: scalable, pythonic fitting

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Click on the links or the images to explore zfit.

What is zfit?

zfit is a (likelihood) model fitting library using analytic distributions such as a Normal, Poisson. It is powerful and highly flexible such that it can be used to analyse data in High Energy Physics experiments like CERN.

(This looks similar to SciPy distributions and fitting, why zfit?)

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What can zfit do

It is built to be powerful and flexible enough to satisfy the strong requirements of High Energy Physics, but is a general purpose library. It focuses strongly on two points, namely:

  • performance: it is built on top of the low-level components of TensorFlow. This is a mathematical library very similar to Numpy but with the native ability to compile parts and imply various optimizations, including GPU support and automatic gradients.

  • custom models: While comparable libraries offer usually a limited set of model combinations or the possibility to implement custom models, zfit supports a variety of composed and multidimensional distributions, such as products and sums, out-of-the-box. Implementing a custom model with your own function (you can try your own shape) is straightforward and allows for arbitrary complicated functions - even functions reaching thousands of lines of code; if analytical methods for integration and sampling are not available, it automatically falls back to numerical methods. No further care needs to be taken by the user therefore.