https://doi.org/10.1140/epjc/s10052-025-15185-8
Regular Article - Theoretical Physics
Colibri: A new tool for fast-flying PDF fits
1
DAMTP, University of Cambridge, Wilberforce Road, CB3 0WA, Cambridge, UK
2
Lucy Cavendish College, Lady Margaret Road, CB3 0BU, Cambridge, UK
3
Instituto de Fisica Corpuscular (IFIC), Universidad de Valencia-CSIC, 46980, Valencia, Spain
a
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Received:
20
October
2025
Accepted:
5
December
2025
Published online:
14
January
2026
Abstract
We present Colibri, 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 Colibri 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 Colibri, 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 Colibri 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.
© The Author(s) 2026
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