Year: 2026
Type: article
Abstract: We present , an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use the built-in functionalities of for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hessian method, the Monte Carlo replica method, and an efficient numerical Bayesian sampling algorithm. To demonstrate the capabilities of , we consider its simplest application: a polynomial PDF parametrisation. We perform closure tests using a full set of DIS data and compare the results of Hessian and Monte Carlo fits with those from a Bayesian fit. We further discuss how the functionalities illustrated in this example can be extended to more complex PDF parametrisations. In particular, the Bayesian framework in provides a principled approach to model selection and model averaging, making it a valuable tool for benchmarking and combining different PDF parametrisations on solid statistical grounds.
Source: The European Physical Journal C
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7124168584