Colibri

Colibri is an adaptable, open-source platform for flexible Parton Distribution Function (PDF) fitting. It is a reportengine app that performs PDF fits with any given parametrisation.

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
  • Citation guidelines
• Tutorials
  • Implementing a PDF model
  • Running Fits
  • Running Closure Tests
  • 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.

[BCCM+21]

Richard D. Ball, Stefano Carrazza, Juan Cruz-Martinez, Luigi Del Debbio, Stefano Forte, Tommaso Giani, Shayan Iranipour, Zahari Kassabov, Jose I. Latorre, Emanuele R. Nocera, Rosalyn L. Pearson, Juan Rojo, Roy Stegeman, Christopher Schwan, Maria Ubiali, Cameron Voisey, and Michael Wilson. An open-source machine learning framework for global analyses of parton distributions. Eur. Phys. J. C, 81(10):958, 2021. arXiv:2109.02671, doi:10.1140/epjc/s10052-021-09747-9.

[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.

[CHM22a]

A. Candido, F. Hekhorn, and G. Magni. N3PDF/eko: Paper (v0.8.5). 2022. Software release. URL: https://doi.org/10.5281/zenodo.6340153, doi:10.5281/zenodo.6340153.

[CHM22b]

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.

[CMMU25]

Mark N. Costantini, Luca Mantani, James M. Moore, and Maria Ubiali. A linear PDF model for Bayesian inference. 7 2025. arXiv:2507.16913.

[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.

[ZCw+25]

Zaharid, Mark N. Costantini, wilsonmr, Juan M. Cruz-Martinez, Roy Stegeman, Cameron Voisey, Alessandro Candido, Stefano Carrazza, and Thomas Kluyver. Reportengine: A framework for declarative data analysis. https://doi.org/10.5281/zenodo.14803041, February 2025. doi:10.5281/zenodo.14803041.

[Buchner16]

Johannes Buchner. A statistical test for Nested Sampling algorithms. Statistics and Computing, 26(1-2):383–392, January 2016. arXiv:1407.5459, doi:10.1007/s11222-014-9512-y.

[Buchner19]

Johannes Buchner. Collaborative Nested Sampling: Big Data versus Complex Physical Models. \pasp , 131(1004):108005, October 2019. arXiv:1707.04476, doi:10.1088/1538-3873/aae7fc.

[Buchner21]

Johannes Buchner. UltraNest - a robust, general purpose Bayesian inference engine. The Journal of Open Source Software, 6(60):3001, April 2021. arXiv:2101.09604, doi:10.21105/joss.03001.

Indices and tables

• Index

• Module Index

• Search Page