Colibri

Colibri is a modern, high-performance framework for fast and flexible PDF fitting. It is a reportengine app to perform PDF fits using arbitrary parametrisations.

The Colibri code

Colibri is available to the public. Along with this online documentation, you can access the code here. The code is made available as an open-source package together with user-friendly examples and the documentation presented here.

If you are a new user head along to Getting started and check out the Tutorials.

Colibri’s workflow

The following diagram presents the workflow of the Colibri code.

Colibri takes as input (i) a PDF model, which may be any arbitrary parametrisation implemented by the user, (ii) JAX, which provides high-performance array operations and native GPU support for fast computations, and (iii) data and theory predictions, which it inherits from the NNPDF framework. It then performs a fit using a given inference method, which is specified by the user. At the time of release, the options are a Monte Carlo, bayesian or analytic fit. In each case, the result follows the LHAPDF format.

The Colibri team

The Colibri collaboration is currently composed of the following members:

• Mark N. Costantini - DAMTP, University of Cambridge

• Luca Mantani - Universidad de Valencia-CSIC

• James M. Moore - DAMTP, University of Cambridge

• Valentina Schütze Sánchez - DAMTP, University of Cambridge

• Maria Ubiali - DAMTP, University of Cambridge

Contents

• Getting started
  • Installing Colibri on Linux or macOS
  • References
• Tutorials
  • Implementing a PDF model
  • Running Fits
  • Running a Closure Test
  • Model-specific Closure Test
  • Colibri Scripts
• Theory
  • Colibri PDF Models
  • Likelihood function
  • Forward Map
  • Prior Distributions
  • Inference Methods
• Functions
  • All Functions
  • Colibri Scripts

Bibliography

[A+05]

S. I. Alekhin and others. DGLAP evolution and parton fits. In HERA and the LHC: A Workshop on the Implications of HERA for LHC Physics: CERN - DESY Workshop 2004/2005 (Midterm Meeting, CERN, 11-13 October 2004; Final Meeting, DESY, 17-21 January 2005), 119–159. Geneva, 2005. CERN. arXiv:hep-ph/0601012.

[BCDC+25]

Andrea Barontini, Mark N. Costantini, Giovanni De Crescenzo, Stefano Forte, and Maria Ubiali. Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data. JHEP, 3 2025. arXiv:2503.17447.

[CHM22]

Alessandro Candido, Felix Hekhorn, and Giacomo Magni. EKO: evolution kernel operators. Eur. Phys. J. C, 82(10):976, 2022. arXiv:2202.02338, doi:10.1140/epjc/s10052-022-10878-w.

[CMMM24]

Mark N. Costantini, Maeve Madigan, Luca Mantani, and James M. Moore. A critical study of the Monte Carlo replica method. JHEP, 12:064, 2024. arXiv:2404.10056, doi:10.1007/JHEP12(2024)064.

[DDGW22]

Luigi Del Debbio, Tommaso Giani, and Michael Wilson. Bayesian approach to inverse problems: an application to NNPDF closure testing. Eur. Phys. J. C, 82(4):330, 2022. arXiv:2111.05787, doi:10.1140/epjc/s10052-022-10297-x.

Indices and tables

• Index

• Module Index

• Search Page