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
Bibliography
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.
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.
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.
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.
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.
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.
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.